International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This document summarizes several papers on implementing feedforward neural networks using field programmable gate arrays (FPGAs). It discusses how FPGAs offer parallelism and flexibility for neural network designs while reducing costs compared to application-specific integrated circuits. The document reviews mathematical models of artificial neurons and different types of neural network architectures. It also examines challenges in efficiently implementing activation functions like the sigmoid on FPGAs. Several papers presented hardware implementations of multilayer feedforward neural networks in VHDL for applications such as digital pre-distortion.
This document discusses using genetic algorithms and evolvable hardware techniques to evolve digital circuit designs, specifically a 1-bit adder circuit. It describes representing circuit designs as chromosomes in a genetic algorithm population. Over generations, genetic operators like crossover and mutation create new circuit designs with the goal of optimizing a fitness function. The best designs are kept and used to populate the next generation. The document outlines compiling and executing provided C++ code to evolve a 1-bit adder circuit and interpret the resulting circuit design using graph data structures like adjacency matrices and lists.
Artificial Neural Network Implementation on FPGA – a Modular ApproachRoee Levy
This document presents an FPGA implementation of an artificial neural network using a modular approach. Key points:
- The implementation uses a multilayer perceptron topology trained with the backpropagation algorithm. It allows networks of any size to be synthesized quickly.
- The design achieves peak performance of 5.46 million connection updates per second during training and 8.24 million predictions per second during computation.
- It was tested on a breast cancer classification problem, achieving 96% accuracy.
- The paper emphasizes important FPGA design principles that make neural network development modular and parameterized. This allows the system to solve various neural network problems efficiently.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
HIGH PERFORMANCE ETHERNET PACKET PROCESSOR CORE FOR NEXT GENERATION NETWORKSijngnjournal
As the demand for high speed Internet significantly increasing to meet the requirement of large data transfers, real-time communication and High Definition ( HD) multimedia transfer over IP, the IP based network products architecture must evolve and change. Application specific processors require high
performance, low power and high degree of programmability is the limitation in many general processor based applications. This paper describes the design of Ethernet packet processor for system-on-chip (SoC) which performs all core packet processing functions, including segmentation and reassembly, packetization classification, route and queue management which will speedup switching/routing performance making it
more suitable for Next Generation Networks (NGN). Ethernet packet processor design can be configured for use with multiple projects targeted to a FPGA device the system is designed to support 1/10/20/40/100 Gigabit links with a speed and performance advantage. VHDL has been used to implement and simulated the required functions in FPGA
First phase report on "ANALYZING THE EFFECTIVENESS OF THE ADVANCED ENCRYPTION...Nikhil Jain
The document analyzes improving the performance of the Advanced Encryption Standard (AES) algorithm using parallel computing on multicore processors. It aims to implement AES using OpenMP to extract parallelism and reduce encryption/decryption times. The methodology divides input data blocks among processor cores to perform encryption/decryption simultaneously. Literature on previous AES parallel implementations is reviewed, highlighting advantages of using OpenMP on multicore CPUs over single-core and GPU approaches. Faster encryption/decryption times are expected compared to sequential processing.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This paper proposes a novel hybrid FPGA architecture that is optimized for both speed and density in floating point applications. The architecture uses both fine-grained units for control logic and bit operations as well as domain-specific coarse-grained units and blocks for datapaths. This allows the resources to be customized for specific application domains like floating point. Experimental results on floating point benchmarks show the proposed architecture can achieve 2.5x speed improvement and 18x area reduction compared to traditional FPGAs.
This document summarizes several papers on implementing feedforward neural networks using field programmable gate arrays (FPGAs). It discusses how FPGAs offer parallelism and flexibility for neural network designs while reducing costs compared to application-specific integrated circuits. The document reviews mathematical models of artificial neurons and different types of neural network architectures. It also examines challenges in efficiently implementing activation functions like the sigmoid on FPGAs. Several papers presented hardware implementations of multilayer feedforward neural networks in VHDL for applications such as digital pre-distortion.
This document discusses using genetic algorithms and evolvable hardware techniques to evolve digital circuit designs, specifically a 1-bit adder circuit. It describes representing circuit designs as chromosomes in a genetic algorithm population. Over generations, genetic operators like crossover and mutation create new circuit designs with the goal of optimizing a fitness function. The best designs are kept and used to populate the next generation. The document outlines compiling and executing provided C++ code to evolve a 1-bit adder circuit and interpret the resulting circuit design using graph data structures like adjacency matrices and lists.
Artificial Neural Network Implementation on FPGA – a Modular ApproachRoee Levy
This document presents an FPGA implementation of an artificial neural network using a modular approach. Key points:
- The implementation uses a multilayer perceptron topology trained with the backpropagation algorithm. It allows networks of any size to be synthesized quickly.
- The design achieves peak performance of 5.46 million connection updates per second during training and 8.24 million predictions per second during computation.
- It was tested on a breast cancer classification problem, achieving 96% accuracy.
- The paper emphasizes important FPGA design principles that make neural network development modular and parameterized. This allows the system to solve various neural network problems efficiently.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
HIGH PERFORMANCE ETHERNET PACKET PROCESSOR CORE FOR NEXT GENERATION NETWORKSijngnjournal
As the demand for high speed Internet significantly increasing to meet the requirement of large data transfers, real-time communication and High Definition ( HD) multimedia transfer over IP, the IP based network products architecture must evolve and change. Application specific processors require high
performance, low power and high degree of programmability is the limitation in many general processor based applications. This paper describes the design of Ethernet packet processor for system-on-chip (SoC) which performs all core packet processing functions, including segmentation and reassembly, packetization classification, route and queue management which will speedup switching/routing performance making it
more suitable for Next Generation Networks (NGN). Ethernet packet processor design can be configured for use with multiple projects targeted to a FPGA device the system is designed to support 1/10/20/40/100 Gigabit links with a speed and performance advantage. VHDL has been used to implement and simulated the required functions in FPGA
First phase report on "ANALYZING THE EFFECTIVENESS OF THE ADVANCED ENCRYPTION...Nikhil Jain
The document analyzes improving the performance of the Advanced Encryption Standard (AES) algorithm using parallel computing on multicore processors. It aims to implement AES using OpenMP to extract parallelism and reduce encryption/decryption times. The methodology divides input data blocks among processor cores to perform encryption/decryption simultaneously. Literature on previous AES parallel implementations is reviewed, highlighting advantages of using OpenMP on multicore CPUs over single-core and GPU approaches. Faster encryption/decryption times are expected compared to sequential processing.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This paper proposes a novel hybrid FPGA architecture that is optimized for both speed and density in floating point applications. The architecture uses both fine-grained units for control logic and bit operations as well as domain-specific coarse-grained units and blocks for datapaths. This allows the resources to be customized for specific application domains like floating point. Experimental results on floating point benchmarks show the proposed architecture can achieve 2.5x speed improvement and 18x area reduction compared to traditional FPGAs.
Spine net learning scale permuted backbone for recognition and localizationDevansh16
Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by ~3% AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.5% AP with a MaskR-CNN detector and achieves 52.1% AP with a RetinaNet detector on COCO for a single model without test-time augmentation, significantly outperforms prior art of detectors. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: this https URL.
This document summarizes research on using memristors to develop nano-scale associative memories (AMs). Key points:
- AMs are important for brain-like computing but scaling traditional circuits is challenging and stored information is fragile.
- Memristors are promising due to their small size and ability to remember resistance levels, but are typically only used as synapses in artificial neural networks (ANNs), not to their full potential.
- The authors develop a framework using genetic programming to automatically design AM circuits using memristors that can outperform traditional ANNs.
- Their results show memristor-based AMs can learn spatial and temporal correlations in inputs, optimize the tradeoff between size and
Brain-Computer Interfaces are communication
systems that use brain signals as commands to a device. Despite
being the only means by which severely paralysed people can
interact with the world most effort is focused on improving and
testing algorithms offline, not worrying about their validation in
real life conditions. The Cybathlon’s BCI-race offers a unique
opportunity to apply theory in real life conditions and fills
the gap. We present here a Neural Network architecture for
the 4-way classification paradigm of the BCI-race able to run
in real-time. The procedure to find the architecture and best
combination of mental commands best suiting this architecture
for personalised used are also described. Using spectral power
features and one layer convolutional plus one fully connected
layer network we achieve a performance similar to that in
literature for 4-way classification and prove that following our
method we can obtain similar accuracies online and offline
closing this well-known gap in BCI performances
This document summarizes the key aspects of a design flow framework that aims to simplify the development of partially reconfigurable systems. The framework hides complexity related to reconfiguration from designers and supports different architectural paradigms and communication infrastructures. It was developed in three phases: studying existing approaches, realizing the framework based on separated tools, and validating it with a new communication protocol. The framework generates architectures from a system description and allows designers to focus on writing modules while handling reconfiguration details.
The document introduces the National Supercomputer Center in Tianjin (NSCC-TJ) and its TH-1A supercomputer system. It describes that NSCC-TJ is sponsored by the Chinese government to provide high performance computing services. It then provides details about the TH-1A system including its hybrid CPU and GPU architecture, proprietary interconnect network, 262TB of memory and 2PB of storage. It also summarizes the system's software stack including the Kylin Linux operating system, compilers, programming environment and visualization system.
This document discusses instruction level power analysis (ILPA) for estimating processor power consumption. It describes how ILPA works by associating an energy cost with each instruction based on its operations and accounting for inter-instruction effects. Initially used for RISC processors, ILPA methods were modified for VLIW/EPIC processors by considering independent energy dissipation across execution slots and clustering similar instructions. ILPA does not provide insight into core power consumption causes but was expanded to microarchitecture-aware models accounting for individual pipeline stages. Register files and caches can also be modeled based on access patterns and state transitions between cycles.
Vlsi IEEE 2014 titles 2014_2015_CSE/IT/ECE/EEE/ STUDENTS IN CHENNAI (S3 INFO...S3 Infotech IEEE Projects
DOTNET/JAVA/MATLAB/VLSI/NS2/EMBEDDED IEEE 2014 PROJECTS FOR ME/BE/B.TECH STUDENTS. FINAL YEAR 2014 PROJECTS FOR CSE/IT/ECE/EEE/ STUDENTS IN CHENNAI (S3 INFOTECH : 09884848198).
Final year IEEE 2014 projects for BE, BTech, ME, MTech &PHD Students (09884848198 : S3 Infotech)
Dear Students,
Greetings from S3 INFOTECH (0988 48 48 198). We are doing Final year (IEEE & APPLICATION) projects in DOTNET, JAVA, MATLAB, ANDROID, VLSI, NS2, EMBEDDED SYSTEMS and POWER ELECTRONICS.
For B.E, M.E, B.Tech, M.Tech, MCA, M.Sc, & PHD Students.
We implement your own IEEE concepts also in ALL Technologies. We are giving support for Journal Arrangement & Publication also.
Send your IEEE base paper to yes3info@gmail.com (or) info@s3computers.com.
To Register your project: www.s3computers.com
We are providing Projects in
• DOT NET
• JAVA / J2EE / J2ME
• EMBEDDED & POWER ELECTRONICS
• MATLAB
• NS2
• VLSI
• NETWORKING
• HADOOP / Bigdata
• Android
• PHP
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
LOGIC OPTIMIZATION USING TECHNOLOGY INDEPENDENT MUX BASED ADDERS IN FPGAVLSICS Design
Adders form an almost obligatory component of every contemporary integrated circuit. The prerequisite of the adder is that it is primarily fast and secondarily efficient in terms of power consumption and chip area. Therefore, careful optimization of the adder is of the greatest importance. This optimization can be attained
in two levels; it can be circuit or logic optimization. In circuit optimization the size of transistors are manipulated, where as in logic optimization the Boolean equations are rearranged (or manipulated) to optimize speed, area and power consumption. This paper focuses the optimization of adder through technology independent mapping. The work presents 20 different logical construction of 1-bit adder cell in CMOS logic and its performance is analyzed in terms of transistor count, delay and power dissipation. These performance issues are analyzed through Tanner EDA with TSMC MOSIS 250nm technology. From this analysis the optimized equation is chosen to construct a full adder circuit in terms of multiplexer. This logic optimized multiplexer based adders are incorporated in selected existing adders like ripple carry
adder, carry look-ahead adder, carry skip adder, carry select adder, carry increment adder and carry save adder and its performance is analyzed in terms of area (slices used) and maximum combinational path delay as a function of size. The target FPGA device chosen for the implementation of these adders was Xilinx ISE 12.1 Spartan3E XC3S500-5FG320. Each adder type was implemented with bit sizes of: 8, 16, 32, 64 bits. This variety of sizes will provide with more insight about the performance of each adder in terms of area and delay as a function of size.
Small introduction to FPGA acceleration and the impact of the new High Level Synthesis toolchains to their programmability
Video here: https://www.linkedin.com/posts/marcobarbone_can-my-application-benefit-from-fpga-acceleration-activity-6848674747375460352-0fua
Performance Optimization of SPH Algorithms for Multi/Many-Core ArchitecturesDr. Fabio Baruffa
In the framework of the Intel Parallel Computing Centre at the Research Campus Garching in Munich, our group at LRZ presents recent results on performance optimization of Gadget-3, a widely used community code for computational astrophysics. We identify and isolate a sample code kernel, which is representative of a typical Smoothed Particle Hydrodynamics (SPH) algorithm and focus on threading parallelism optimization, change of the data layout into Structure of Arrays (SoA), compiler auto-vectorization and algorithmic improvements in the particle sorting. We measure lower execution time and improved threading scalability both on Intel Xeon (2.6× on Ivy Bridge) and Xeon Phi (13.7× on Knights Corner) systems. First tests on second generation Xeon Phi (Knights Landing) demonstrate the portability of the devised optimization solutions to upcoming architectures.
ScalableCore System: A Scalable Many-core Simulator by Employing Over 100 FPGAsShinya Takamaeda-Y
The document summarizes a presentation about the ScalableCore System, a scalable many-core simulator that employs over 100 FPGAs. It maps a target many-core processor across multiple FPGA boards, each simulating a tile/core. This allows achieving scalable simulation speeds as the number of target cores increases. Evaluation shows the resource usage and faster simulation speeds compared to software simulators as the number of simulated nodes increases from 16 to 100.
Hardware Acceleration for Machine LearningCastLabKAIST
This document provides an overview of a lecture on hardware acceleration for machine learning. The lecture will cover deep neural network models like convolutional neural networks and recurrent neural networks. It will also discuss various hardware accelerators developed for machine learning, including those designed for mobile/edge and cloud computing environments. The instructor's background and the agenda topics are also outlined.
Towards Automated Design Space Exploration and Code Generation using Type Tra...waqarnabi
Slides for talk at First International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC'15), Austin, Texas, Nov 15 2015. Held in conjunction with SC15.
The subject of this study is to show the application of fuzzy logic in image processing with a brief introduction to fuzzy logic and digital image processing.
Using Many-Core Processors to Improve the Performance of Space Computing Plat...Fisnik Kraja
This document summarizes a presentation on introducing many-core and commercial off-the-shelf (COTS) technologies into satellite computing architectures. It discusses constraints of onboard computers like power, size, heat, and high reliability requirements. It proposes a new architecture using many-core processors and COTS products to improve processing abilities while maintaining reliability through hardware and software fault tolerance techniques. It evaluates the architecture on synthetic aperture radar benchmarking and finds performance scales linearly with cores. Combining COTS with radiation-hardened components could provide over 100x speedup and improved reliability, flexibility, and portability.
International Journal of Computational Engineering Research (IJCER) ijceronline
This document describes a system for implementing an artificial neuron using an FPGA. The system first converts analog signals from electrochemical sensors to digital signals using a 12-bit analog-to-digital converter (ADC). It then implements the mathematical operations of a neuron in digital logic on the FPGA, including multiplication, accumulation, and an activation function. Simulation and chipscope results are presented which verify the design and operation of the artificial neuron on the FPGA board. The system provides a modular design that could be expanded to create a complete artificial neural network for processing electrochemical sensor data.
The story is about a boy who had a bad temper. His father gave him nails to hammer into a fence every time he lost his temper. Over weeks of controlling his anger, the number of nails hammered daily decreased. When he went one day without losing his temper, his father had him pull out nails each following day he remained calm. Eventually all the nails were removed, but the fence was left with holes, demonstrating that angry words leave lasting scars like physical wounds.
Spine net learning scale permuted backbone for recognition and localizationDevansh16
Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by ~3% AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.5% AP with a MaskR-CNN detector and achieves 52.1% AP with a RetinaNet detector on COCO for a single model without test-time augmentation, significantly outperforms prior art of detectors. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: this https URL.
This document summarizes research on using memristors to develop nano-scale associative memories (AMs). Key points:
- AMs are important for brain-like computing but scaling traditional circuits is challenging and stored information is fragile.
- Memristors are promising due to their small size and ability to remember resistance levels, but are typically only used as synapses in artificial neural networks (ANNs), not to their full potential.
- The authors develop a framework using genetic programming to automatically design AM circuits using memristors that can outperform traditional ANNs.
- Their results show memristor-based AMs can learn spatial and temporal correlations in inputs, optimize the tradeoff between size and
Brain-Computer Interfaces are communication
systems that use brain signals as commands to a device. Despite
being the only means by which severely paralysed people can
interact with the world most effort is focused on improving and
testing algorithms offline, not worrying about their validation in
real life conditions. The Cybathlon’s BCI-race offers a unique
opportunity to apply theory in real life conditions and fills
the gap. We present here a Neural Network architecture for
the 4-way classification paradigm of the BCI-race able to run
in real-time. The procedure to find the architecture and best
combination of mental commands best suiting this architecture
for personalised used are also described. Using spectral power
features and one layer convolutional plus one fully connected
layer network we achieve a performance similar to that in
literature for 4-way classification and prove that following our
method we can obtain similar accuracies online and offline
closing this well-known gap in BCI performances
This document summarizes the key aspects of a design flow framework that aims to simplify the development of partially reconfigurable systems. The framework hides complexity related to reconfiguration from designers and supports different architectural paradigms and communication infrastructures. It was developed in three phases: studying existing approaches, realizing the framework based on separated tools, and validating it with a new communication protocol. The framework generates architectures from a system description and allows designers to focus on writing modules while handling reconfiguration details.
The document introduces the National Supercomputer Center in Tianjin (NSCC-TJ) and its TH-1A supercomputer system. It describes that NSCC-TJ is sponsored by the Chinese government to provide high performance computing services. It then provides details about the TH-1A system including its hybrid CPU and GPU architecture, proprietary interconnect network, 262TB of memory and 2PB of storage. It also summarizes the system's software stack including the Kylin Linux operating system, compilers, programming environment and visualization system.
This document discusses instruction level power analysis (ILPA) for estimating processor power consumption. It describes how ILPA works by associating an energy cost with each instruction based on its operations and accounting for inter-instruction effects. Initially used for RISC processors, ILPA methods were modified for VLIW/EPIC processors by considering independent energy dissipation across execution slots and clustering similar instructions. ILPA does not provide insight into core power consumption causes but was expanded to microarchitecture-aware models accounting for individual pipeline stages. Register files and caches can also be modeled based on access patterns and state transitions between cycles.
Vlsi IEEE 2014 titles 2014_2015_CSE/IT/ECE/EEE/ STUDENTS IN CHENNAI (S3 INFO...S3 Infotech IEEE Projects
DOTNET/JAVA/MATLAB/VLSI/NS2/EMBEDDED IEEE 2014 PROJECTS FOR ME/BE/B.TECH STUDENTS. FINAL YEAR 2014 PROJECTS FOR CSE/IT/ECE/EEE/ STUDENTS IN CHENNAI (S3 INFOTECH : 09884848198).
Final year IEEE 2014 projects for BE, BTech, ME, MTech &PHD Students (09884848198 : S3 Infotech)
Dear Students,
Greetings from S3 INFOTECH (0988 48 48 198). We are doing Final year (IEEE & APPLICATION) projects in DOTNET, JAVA, MATLAB, ANDROID, VLSI, NS2, EMBEDDED SYSTEMS and POWER ELECTRONICS.
For B.E, M.E, B.Tech, M.Tech, MCA, M.Sc, & PHD Students.
We implement your own IEEE concepts also in ALL Technologies. We are giving support for Journal Arrangement & Publication also.
Send your IEEE base paper to yes3info@gmail.com (or) info@s3computers.com.
To Register your project: www.s3computers.com
We are providing Projects in
• DOT NET
• JAVA / J2EE / J2ME
• EMBEDDED & POWER ELECTRONICS
• MATLAB
• NS2
• VLSI
• NETWORKING
• HADOOP / Bigdata
• Android
• PHP
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
LOGIC OPTIMIZATION USING TECHNOLOGY INDEPENDENT MUX BASED ADDERS IN FPGAVLSICS Design
Adders form an almost obligatory component of every contemporary integrated circuit. The prerequisite of the adder is that it is primarily fast and secondarily efficient in terms of power consumption and chip area. Therefore, careful optimization of the adder is of the greatest importance. This optimization can be attained
in two levels; it can be circuit or logic optimization. In circuit optimization the size of transistors are manipulated, where as in logic optimization the Boolean equations are rearranged (or manipulated) to optimize speed, area and power consumption. This paper focuses the optimization of adder through technology independent mapping. The work presents 20 different logical construction of 1-bit adder cell in CMOS logic and its performance is analyzed in terms of transistor count, delay and power dissipation. These performance issues are analyzed through Tanner EDA with TSMC MOSIS 250nm technology. From this analysis the optimized equation is chosen to construct a full adder circuit in terms of multiplexer. This logic optimized multiplexer based adders are incorporated in selected existing adders like ripple carry
adder, carry look-ahead adder, carry skip adder, carry select adder, carry increment adder and carry save adder and its performance is analyzed in terms of area (slices used) and maximum combinational path delay as a function of size. The target FPGA device chosen for the implementation of these adders was Xilinx ISE 12.1 Spartan3E XC3S500-5FG320. Each adder type was implemented with bit sizes of: 8, 16, 32, 64 bits. This variety of sizes will provide with more insight about the performance of each adder in terms of area and delay as a function of size.
Small introduction to FPGA acceleration and the impact of the new High Level Synthesis toolchains to their programmability
Video here: https://www.linkedin.com/posts/marcobarbone_can-my-application-benefit-from-fpga-acceleration-activity-6848674747375460352-0fua
Performance Optimization of SPH Algorithms for Multi/Many-Core ArchitecturesDr. Fabio Baruffa
In the framework of the Intel Parallel Computing Centre at the Research Campus Garching in Munich, our group at LRZ presents recent results on performance optimization of Gadget-3, a widely used community code for computational astrophysics. We identify and isolate a sample code kernel, which is representative of a typical Smoothed Particle Hydrodynamics (SPH) algorithm and focus on threading parallelism optimization, change of the data layout into Structure of Arrays (SoA), compiler auto-vectorization and algorithmic improvements in the particle sorting. We measure lower execution time and improved threading scalability both on Intel Xeon (2.6× on Ivy Bridge) and Xeon Phi (13.7× on Knights Corner) systems. First tests on second generation Xeon Phi (Knights Landing) demonstrate the portability of the devised optimization solutions to upcoming architectures.
ScalableCore System: A Scalable Many-core Simulator by Employing Over 100 FPGAsShinya Takamaeda-Y
The document summarizes a presentation about the ScalableCore System, a scalable many-core simulator that employs over 100 FPGAs. It maps a target many-core processor across multiple FPGA boards, each simulating a tile/core. This allows achieving scalable simulation speeds as the number of target cores increases. Evaluation shows the resource usage and faster simulation speeds compared to software simulators as the number of simulated nodes increases from 16 to 100.
Hardware Acceleration for Machine LearningCastLabKAIST
This document provides an overview of a lecture on hardware acceleration for machine learning. The lecture will cover deep neural network models like convolutional neural networks and recurrent neural networks. It will also discuss various hardware accelerators developed for machine learning, including those designed for mobile/edge and cloud computing environments. The instructor's background and the agenda topics are also outlined.
Towards Automated Design Space Exploration and Code Generation using Type Tra...waqarnabi
Slides for talk at First International Workshop on Heterogeneous High-performance Reconfigurable Computing (H2RC'15), Austin, Texas, Nov 15 2015. Held in conjunction with SC15.
The subject of this study is to show the application of fuzzy logic in image processing with a brief introduction to fuzzy logic and digital image processing.
Using Many-Core Processors to Improve the Performance of Space Computing Plat...Fisnik Kraja
This document summarizes a presentation on introducing many-core and commercial off-the-shelf (COTS) technologies into satellite computing architectures. It discusses constraints of onboard computers like power, size, heat, and high reliability requirements. It proposes a new architecture using many-core processors and COTS products to improve processing abilities while maintaining reliability through hardware and software fault tolerance techniques. It evaluates the architecture on synthetic aperture radar benchmarking and finds performance scales linearly with cores. Combining COTS with radiation-hardened components could provide over 100x speedup and improved reliability, flexibility, and portability.
International Journal of Computational Engineering Research (IJCER) ijceronline
This document describes a system for implementing an artificial neuron using an FPGA. The system first converts analog signals from electrochemical sensors to digital signals using a 12-bit analog-to-digital converter (ADC). It then implements the mathematical operations of a neuron in digital logic on the FPGA, including multiplication, accumulation, and an activation function. Simulation and chipscope results are presented which verify the design and operation of the artificial neuron on the FPGA board. The system provides a modular design that could be expanded to create a complete artificial neural network for processing electrochemical sensor data.
The story is about a boy who had a bad temper. His father gave him nails to hammer into a fence every time he lost his temper. Over weeks of controlling his anger, the number of nails hammered daily decreased. When he went one day without losing his temper, his father had him pull out nails each following day he remained calm. Eventually all the nails were removed, but the fence was left with holes, demonstrating that angry words leave lasting scars like physical wounds.
El documento presenta un libro sobre una emoción elegida por un equipo. El libro contiene secciones sobre la definición de la emoción, sinónimos y antónimos, ilustraciones, fotografías, expresiones y sensaciones asociadas, situaciones en las que se experimenta la emoción, sonidos y música relacionados, noticias, y conclusiones sobre la emoción. El libro está diseñado para ser personalizado por el equipo con la información que deseen incluir.
Technology can provide helpful tools and resources for students with autism. The presentation outlined a framework for discussing technology solutions and provided examples of tools that have an evidence base to support their use. It also included checklists to help professionals implement appropriate technology options for their students.
This document discusses Moodle, an open-source learning management system. It provides key facts about Moodle such as its history, focus on pedagogy, and growing international community. Data is presented showing high usage of Moodle at Worcester College of Technology, including 1000 courses and 13,000 daily hits across their Moodle platforms. Reasons for Moodle's success include its cost, ease of use, functionality, adaptability and support community. Moodle allows reaching students anywhere and learning at their own pace through various features and integration of multimedia content.
Softmax function is an integral part of object detection frameworks based on most deep or shallow neural
networks. While the configuration of different operation layers in a neural network can be quite different,
softmax operation is fixed. With the recent advances in object detection approaches, especially with the
introduction of highly accurate convolutional neural networks, researchers and developers have suggested
different hardware architectures to speed up the overall operation of these compute-intensive algorithms.
Xilinx, one of the leading FPGA vendors, has recently introduced a deep neural network development kit for
exactly this purpose. However, due to the complex nature of softmax arithmetic hardware involving
exponential function, this functionality is only available for bigger devices. For smaller devices, this operation is
bound to be implemented in software. In this paper, a light-weight hardware implementation of this function
has been proposed which does not require too many logic resources when implemented on an FPGA device.
The proposed design is based on the analysis of the statistical properties of a custom convolutional neural
network when used for classification on a standard dataset i.e. CIFAR-10. Specifically, instead of using a brute
force approach to design a generic full precision arithmetic circuit for SoftMax function using real numbers, an
approximate integer-only design has been suggested for the limited range of operands encountered in realworld
scenario. The approximate circuit uses fewer logic resources since it involves computing only a few
iterations of the series expansion of exponential function. However, despite using fewer iterations, the function
has been shown to work as good as the full precision circuit for classification and leads to only minimal error
being introduced in the associated probabilities. The circuit has been synthesized using Hardware Description
Language (HDL) Coder and Vision HDL toolboxes in Simulink® by Mathworks® which provide higher level
abstraction of image processing and machine learning algorithms for quick deployment on a variety of target
hardware. The final design has been implemented on a Xilinx FPGA development board i.e. Zedboard which
contains the necessary hardware components such as USB, Ethernet and HDMI interfaces etc. to implement a
fully working system capable of processing a machine learning application in real-time.
International Refereed Journal of Engineering and Science (IRJES)irjes
International Refereed Journal of Engineering and Science (IRJES) is a leading international journal for publication of new ideas, the state of the art research results and fundamental advances in all aspects of Engineering and Science. IRJES is a open access, peer reviewed international journal with a primary objective to provide the academic community and industry for the submission of half of original research and applications
A Collaborative Research Proposal To The NSF Research Accelerator For Multip...Scott Donald
This document proposes a collaborative research project called RAMP (Research Accelerator for Multiple Processors) to build a shared experimental parallel hardware/software platform using FPGAs. It aims to overcome limitations of simulation-based research and enable faster hardware-software co-design. By providing infrastructure, models, and tools on top of FPGAs, RAMP would lower barriers to entry and facilitate cross-disciplinary research on parallel computing challenges. The proposal seeks additional funding to develop the platform beyond an initial NSF award by integrating models from multiple universities and addressing issues identified.
A SURVEY OF NEURAL NETWORK HARDWARE ACCELERATORS IN MACHINE LEARNING mlaij
The use of Machine Learning in Artificial Intelligence is the inspiration that shaped technology as it is today. Machine Learning has the power to greatly simplify our lives. Improvement in speech recognition and language understanding help the community interact more naturally with technology. The popularity of machine learning opens up the opportunities for optimizing the design of computing platforms using welldefined hardware accelerators. In the upcoming few years, cameras will be utilised as sensors for several applications. For ease of use and privacy restrictions, the requested image processing should be limited to a local embedded computer platform and with a high accuracy. Furthermore, less energy should be consumed. Dedicated acceleration of Convolutional Neural Networks can achieve these targets with high flexibility to perform multiple vision tasks. However, due to the exponential growth in technology constraints (especially in terms of energy) which could lead to heterogeneous multicores, and increasing number of defects, the strategy of defect-tolerant accelerators for heterogeneous multi-cores may become a main micro-architecture research issue. The up to date accelerators used still face some performance issues such as memory limitations, bandwidth, speed etc. This literature summarizes (in terms of a survey) recent work of accelerators including their advantages and disadvantages to make it easier for developers with neural network interests to further improve what has already been established.
This document describes a test architecture that separates parallel program communication from computation kernels to enable future partial dynamic reconfiguration of processing elements (PEs) on FPGAs. The architecture implements static softcore processors as test PEs on a Xilinx Virtex 5 FPGA. One PE acts as a host cell running MPI for communication, while other PEs act as computing cells running computation kernels. The NAS Parallel Benchmarks integer sort is used to benchmark communication and computation performance on this architecture.
FPGA IMPLEMENTATION OF PRIORITYARBITER BASED ROUTER DESIGN FOR NOC SYSTEMSIAEME Publication
An efficient Priority-Arbiter based Router is designed along with 2X2 and 3X3 mesh
topology based NOC architecture are designed. The Priority –Arbiter based Router
design includes Input registers, Priority arbiter, and XY- Routing algorithm. The
Priority-Arbiter based Router and NOC 2X2 and 3X3 Router designs are synthesized
and implemented using Xilinx ISE Tool and simulated using Modelsim6.5f. The
implementation is done by Artix-7 FPGA device, and the physically debugging of the
NOC 2X2 Router design is verified using Chipscope pro tool. The performance results
are analyzed in terms of the Area (Slices, LUT’s), Timing period, and Maximum
operating frequency. The comparison of the Priority-Arbiter based Router is made
concerning previous similar architecture with improvements.
FPGA IMPLEMENTATION OF PRIORITYARBITER BASED ROUTER DESIGN FOR NOC SYSTEMSIAEME Publication
An efficient Priority-Arbiter based Router is designed along with 2X2 and 3X3 mesh
topology based NOC architecture are designed. The Priority –Arbiter based Router
design includes Input registers, Priority arbiter, and XY- Routing algorithm. The
Priority-Arbiter based Router and NOC 2X2 and 3X3 Router designs are synthesized
and implemented using Xilinx ISE Tool and simulated using Modelsim6.5f. The
implementation is done by Artix-7 FPGA device, and the physically debugging of the
NOC 2X2 Router design is verified using Chipscope pro tool. The performance results
are analyzed in terms of the Area (Slices, LUT’s), Timing period, and Maximum
operating frequency. The comparison of the Priority-Arbiter based Router is made
concerning previous similar architecture with improvements.
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Implementation of Feed Forward Neural Network for Classification by Education...ijsrd.com
in the last few years, the electronic devices production field has witness a great revolution by having the new birth of the extraordinary FPGA (Field Programmable Gate Array) family platforms. These platforms are the optimum and best choice for the modern digital systems now a day. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. In this paper a hardware design of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. Digital system architecture is designed to realize a feed forward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL).General Terms-Network.
Many intellectual property (IP) modules are present in contemporary system on chips (SoCs). This could provide an issue with interconnection among different IP modules, which would limit the system's ability to scale. Traditional bus-based SoC architectures have a connectivity bottleneck, and network on chip (NoC) has evolved as an embedded switching network to address this issue. The interconnections between various cores or IP modules on a chip have a significant impact on communication and chip performance in terms of power, area latency and throughput. Also, designing a reliable fault tolerant NoC became a significant concern. In fault tolerant NoC it becomes critical to identify faulty node and dynamically reroute the packets keeping minimum latency. This study provides an insight into a domain of NoC, with intention of understanding fault tolerant approach based on the XY routing algorithm for 4×4 mesh architecture. The fault tolerant NoC design is synthesized on field programmable gate array (FPGA).
A simplified design of multiplier for multi layer feed forward hardware neura...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Dynamic classification in silicon-based forwarding engine environmentsTal Lavian Ph.D.
Current network devices enable connectivity between end systems with support for routing with a defined set of protocol software bundled with the hardware. These devices do not support user customization or the introduction of new software applications. Programmable network devices allow for the dynamic downloading of customized programs into network devices allowing for the introduction of new protocols and network services. The Oplet Runtime Environment (ORE) is a programmable network architecture built on a Gigabit Ethernet L3 Routing Switch to support downloadable services. Complementing the ORE, we introduce the JFWD API, a uniform, platform-independent portal through which application programmers control the forwarding engines of heterogeneous network nodes (e.g., switches and routers). Using the JFWD API, an ORE service has been implemented to classify and dynamically adjust packet handling on silicon-based network devices.
This document describes the development of a sensor node for environmental monitoring using reconfigurable system-on-chip (RSOC) technology. The main components of the sensor node, including a pixel sensor, reconfigurable processing core, and a tiny solar unit for recharging, are integrated onto a single FPGA chip. The use of FPGA enables remote hardware reconfiguration but also increases power consumption. A tiny solar unit helps address this issue by recharging the node's battery. Potential applications include border control and forest fire monitoring.
Artificial Neural Network Implementation On FPGA ChipMaria Perkins
This document discusses implementing artificial neural networks on field programmable gate arrays (FPGAs). It begins with an abstract noting ANNs have been mostly implemented in software, but hardware versions can provide better performance. The document then reviews work done by various researchers on implementing ANNs using FPGAs. It discusses the structure of artificial neurons and feedforward neural networks. It also provides an overview of VHDL and describes different architectures for implementing artificial neurons in hardware using FPGAs. The goal is to help young researchers in implementing and realizing ANNs with hardware.
O Field-programmable gate arrays (FPGAs) suffer from lower application design productivity than other devices due to compilation taking hours or days. To address this, the paper evaluates "intermediate fabrics" (IFs), which are virtual reconfigurable architectures implemented on FPGAs that enable near-instant placement and routing. An IF for image processing is designed with resources like multipliers, adders, and shift registers. Experimental results show the IF provides a 700x speedup in placement and routing compared to vendor tools, with modest overhead of 7% in performance and 34-44% in area. IFs have the potential to significantly improve FPGA design productivity.
Network Function Modeling and Performance EstimationIJECEIAES
This work introduces a methodology for the modelization of network functions focused on the identification of recurring execution patterns as basic building blocks and aimed at providing a platform independent representation. By mapping each modeling building block on specific hardware, the performance of the network function can be estimated in terms of maximum throughput that the network function can achieve on the specific execution platform. The approach is such that once the basic modeling building blocks have been mapped, the estimate can be computed automatically for any modeled network function. Experimental results on several sample network functions show that although our approach cannot be very accurate without taking in consideration traffic characteristics, it is very valuable for those application where even loose estimates are key. One such example is orchestration in network functions virtualization (NFV) platforms, as well as in general virtualization platforms where virtual machine placement is based also on the performance of network services offered to them. Being able to automatically estimate the performance of a virtualized network function (VNF) on different execution hardware, enables optimal placement of VNFs themselves as well as the virtual hosts they serve, while efficiently utilizing available resources.
FPGA BASED VLSI DESIGN
FPGAs allow designers to emulate IC designs using programming languages like VHDL and Verilog before final hardware implementation. FPGAs contain programmable logic blocks and interconnects that can be configured to implement different digital circuits. Common FPGA architectures include a 2D array of configurable logic blocks and routing channels that can be programmed to connect logic blocks according to a design. FPGAs offer advantages like reprogrammability, fast development times, and performance gains for software applications.
Run-Time Adaptive Processor Allocation of Self-Configurable Intel IXP2400 Net...CSCJournals
An ideal Network Processor, that is, a programmable multi-processor device must be capable of offering both the flexibility and speed required for packet processing. But current Network Processor systems generally fall short of the above benchmarks due to traffic fluctuations inherent in packet networks, and the resulting workload variation on individual pipeline stage over a period of time ultimately affects the overall performance of even an otherwise sound system. One potential solution would be to change the code running at these stages so as to adapt to the fluctuations; a near robust system with standing traffic fluctuations is the dynamic adaptive processor, reconfiguring the entire system, which we introduce and study to some extent in this paper. We achieve this by using a crucial decision making model, transferring the binary code to the processor through the SOAP protocol.
Similar to International Journal of Computational Engineering Research(IJCER) (20)
Run-Time Adaptive Processor Allocation of Self-Configurable Intel IXP2400 Net...
International Journal of Computational Engineering Research(IJCER)
1. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8
Mapping Fpga To Field Programmableneural Network Array(Fpnna)
1
,H Bhargav, 2, Dr. Nataraj K. R
1,
Assistant Professor,.Vidyavardhaka College of Engineering, Mysore, Karnataka, India.
2,.
Professor,.SJB Institute of Technology, Bangalore, Karnataka, India.
Abstract
My paper presents the implementation of a generalized back-propagation mu ltilayer perceptron (MLP)
architecture, on FPGA , described in VLSI hard ware description language (VHDL). The develop ment of hardware
platforms is not very economical because of the high hardware cost and quantity of the arith metic operations required in
online artificial neural networks (ANNs), i.e., general purpose ANNs with learning capability. Besides, there remains a
dearth of hardware platforms for design space explo ration, fast prototyping, and testing of these networks. Our general
purpose architecture seeks to fill that gap and at the same time serve as a tool to gain a better understanding of issues
unique to ANNs implemented in hardware, part icularly using field programmable gate array (FPGA).This work describes a
platform that offers a high degree of parameterization, while maintaining generalized network design with performance
comparable to other hardware-based MLP imp lementations. Application of the hardware implementation of ANN with
back-propagation learning algorith m for a realistic applicat ion is also presented.
Index Terms:Back-propagation, field programmable gate array (FPGA ), hardware imp lementation, mult ilayer
perceptron, neural network, NIR spectra calib ration, spectroscopy, VHDL, Xilin x FPGA .
1. INTRODUCTION
In recent years, artificial neural networks have been widely implemented in several research areas such as image
processing, speech processing and medical diagnoses. The reason of this widely imp lementation is their high classification
power and learn ing ability. At the present time most of these networks are simulated by software programs or fabricated
using VLSI technology [9]. The software simulat ion needs a microprocessor and usually takes a long period of time to
execute the huge number of computations involved in the operation of the network. Several researchers have adopted
hardware implementations to realize such networks [8]&[12].This realizat ion makes the network stand alone and operate
on a real-time fashion. Recently, implementation of Field Programmable Gate Arrays (FPGA's) in realizing comp lex
hardware system has been accelerated [7].Field programmable gate arrays are h igh-density digital integrated circuits that
can be configured by the user; they combine the flexibility of gate arrays with desktop programmability. An ANN’s ability
to learn and solve problems relies in part on the structural Characteristics of that network. Those characteristics include the
number of layers in a network, the number o f neurons per layer, and the activation functions of those neurons, etc. There
remains a lack of a reliable means for determin ing the optimal set of network characteristics for a given application.
Nu merous imp lementations of ANNs already exist [5]–[8], but most of them being in software on sequential processors
[2]. Soft ware imp lementations can be quickly constructed, adapted, and tested for a wide range of applications. However,
in some cases, the use of hardware architectures matching the parallel structure of ANNs is desirable to optimize
performance or reduce the cost of the imp lementation, particularly for applicat ions demanding high performance [9], [10].
Unfortunately, hardware p latforms suffer fro m several unique disadvantages such as difficu lties in achieving high data
precision with relat ion to hardware cost, the high hardware cost of the necessary calculations, and the inflexibility of the
platform as co mpared to software.In our work, we have attempted to address some of these disadvantages by imp lementing
a field programmable gate array (FPGA )-based architecture of a neural network with learn ing capability because FPGAs
are high-density digital integrated circuits that can be configured by the user; they combine the flexib ility of g ate arrays
with desktop programmab ility.Their architecture consists mainly of: Configurab le Logic Blocks (CLB's) where Boolean
functions can be realized, Input output Blocks (IOB's) serve as input output ports, and programmab le interconnection
between the CLB's and IOB's.
2. MOTIVATION
Features of ANN support evaluation imp lementations of different implementations of networks by changing
parameters such as the number of neurons per layer, number of layers & the synaptic weights. ANNs have three main
characteristics: parallelism, modularity & dynamic adaptation. Parallelism means that all neurons in the same layer perform
the computation simultaneously. Modularity refers to the fact that neurons have the same structural architecture. It is clear
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2. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8
fro m these characteristics that FPGAs are well tailored to support implementation of ANNs, since it has a regular structure
based on a matrix of parallel configurable units.Imp lementations in Application Specific Integrated circuits (ASICs) lack
flexibility for evaluating the performance of different implementations. This deficiency can be overcome by using
Programmable Logic Devices (PLDs) such as FPGAs. FPGAs provide high performance for parallel computation &
enhanced flexibility (if co mpared with ASICs imp lementation) & are the best candidates for this kind of hardware
implementations. If we mount ANN on FPGA , design should be such that there should be a good balance between the
response & area restrictions of ANN on FPGA.FPGAs are programmable logic devices that permit the implementation of
digital systems. They provide arrays of logical cells that can be configured to perform given functions by means of
configuring bit stream. An FPGA can have its behavior redefined in such a way that it can implement completely different
digital systems on the same chip. Despite the prevalence of software-based ANN implementations, FPGAs and similarly,
application specific integrated circuits (ASICs) have attracted much interest as platforms for ANNs because of the
perception that their natural potential for parallelis m and entirely hardware-based computation implementation provide better
performance than their predominantly sequential software-based counterparts. As a consequence hardware-based
implementations came to be preferred for high performance ANN applications [9]. While it is broadly assumed, it should be
noted that an empirical study has yet to confirm that hardware-based platforms for ANNs provide higher levels of
performance than software in all the cases [10]. Currently, no well defined methodology exists to determine the optimal
architectural properties (i.e., nu mber of neurons, number of layers, type of squashing function, etc.) of a neural network fo r a
given application. The only method currently available to us is a sys tematic approach of educated trial and error. Software
tools like MATLAB Neural Network Toolbo x [13] make it relatively easy for us to quickly simulate and evaluate various
ANN configurations to find an optimal architecture for software imp lementations. In hardware, there are more network
characteristics to consider, many dealing with precision related issues like data and computational precision. Similar
simulation or fast prototyping tools for hardware are not well developed.
Consequently, our primary interest in FPGAs lies in their reconfigurability. By exp loit ing the reconfigurability of
FPGAs, we aim to transfer the flexib ility of parameterized software based ANNs and ANN simulators to hardware
platforms. All these features of ANN & FPGAs have made me think about giving a hardware(FPGA) platform for ANN.
Doing this, we will give the user the same ability to efficiently exp lore the design space and prototype in hardware as is no w
possible in software. Additionally, with such a tool we will be able to gain some insight into hardware specific issues such as
the effect of hardware implementation and design decisions on performance, accuracy, and design size.
3. PREVIOUS WORKS
In the paper published by Benjamin Schrauwen,1, Michiel D’Haene 2, David Verstraeten 2, Jan Van Campenhout
in the year 2008 with the title Co mpact hardware Liquid State Machines on FPGA for real-t ime speech recognition have
proposed that real-time speech recognition is possible on limited FPGA hardware using an LSM. To attain this we first
exp lored existing hardware architectures (which we reimplemented and improved) for compact implementation of SNNs.
These designs are however more than 200 times faster than real-time which is not desired because lots of hardware resources
are spend on speed that is not needed. We present a novel hardware architecture based on serial processing of dendritic trees
using serial arithmetic. It easily and compactly allows a scalable number of PEs to process larger net - works in parallel.
Using a hardware oriented RC design flow we were able to easily port the existing speech recognition application to the
actual quantized hardware architecture. For future work we plan to investigate different applications, such as autonomous
robot control, large vocabulary speech recognition, and medical signal processing, that all use the hardware LSM
architectures presented in this work, but which all have very different area/speed trade -offs. Parameter changing without
resynthesis will also be investigated (dynamic reconfiguration or parameter pre-run shift-in with a long scan-chain are
possibilities).In the paper published by Subbarao Tatikonda, Student Member, IEEE, Pramod Agarwal, Member, IEEE in the
year 2008 with the title Field Programmab le Gate Array (FPGA) Based Neural Network Implementation of Motion Control
and Fault Diagnosis of Induction Motor Drive have proposed A study of fault tolerant strategy on the ANN-SVPWM VSI
performed. This Strategy is based on the reconfiguration on the inverter topology after occurrence . The modified topology
for the inverter is proposed. Entire system design on FPGA has been suggested which includes the programmable low -pass
filter flu x estimation, space vector PWM (neural network based), fault diagnosis block and binary logic block. Th e paper
talks about the fault feature extraction and classification and then how the neural networks can be build on the FPGA. Digita l
circuits models for the linear and log sigmo id been discussed. This work clearly gives the observer no slight change in
operation with any fault in one leg. Study suggests that feature extraction is a challenging research topic still to be explo ited.
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3. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8
Still this work has many prospects in multilevel inverters, where the better operating algorithms can be proposed with
increase in the level of inverters. The number of redundant states is more they have to be exploited in the near future work.
The system behaves as no fault in the system at all.
4. NEURAL NETWROK M ODEL D ES CRIPTION
There are various hardware implementations based on ASIC,DSP & FPGA .DSP based implementation is
sequential and hence does not preserve the parallel architecture of the neurons in a layer. ASIC imp lementations do not offer
reconfigurablity by the user. FPGA is a suitable hardware for neural network implementation as it preserves the parallel
architecture of the neurons in a layer and offers flexibility in reconfiguration. FPGA realization of ANNs with a large
number of neurons is a challenging task. Selecting weight precision is one of the important ch oices when implementing
ANNs on FPGAs. Weight precision is used to trade-off the capabilit ies of the realized ANNs against the implementation
cost. A higher weight precision means fewer quantization errors in the final implementations, while a lower precis ion leads
to simpler designs, greater speed and reductions in area requirements and power consumption. One way of resolving the
trade-off is to determine the ―minimu m precision‖ required. In this work we consider the weights as 8 -bit fixed-point values.
Direct implementation for non-linear sigmoid transfer functions is very expensive. As the excitation function is highly
nonlinear we adopt the Look Up Table (LUT) method in order to simp lify function computation. The LUT is imp lemented
using the inbuilt RAM available in FPGA IC.
ROM
(Weights)
LUT Output Output
Multiplier + Accumulator LUT
Register
Input
Fig 1. Neuron RTL Block Diagram
The use of LUTs reduces the resource requirement and improves the speed. Also the implementation of LUT needs
no external RAM since the inbuilt memory is sufficient to implement the excitation function. The basic structure of the
functional unit (neuron) that implements the calculations associated with neuron. Each neuron has a ROM, which stores the
weights of the connection links between the particular neuron to the neu rons of the previous layer. The multiplier performs
high speed multiplication of input signals with weights from ROM . Multiplier is again imp lemented using an LUT
mu ltiplier. Such implementation of a multiplier needs one of the operands to be constant. In this case the other operand
addresses the LUT where the result of multip lication is previously stored. Given two operands A&B with n & m bits
respectively & B is constant, it is possible to implement their mu ltiplication in LUT of 2n . Since both mult iplier & Activation
Function(sigmoid function) are imp lemented using LUT, cost of implementation is very much reduced. We have tried
comparing the model in the reference no. [15], with that of our model with respect to cost of implementation, in the
conclusion section. A sixteen bit register is used to hold the weights from the ROM and the input signal fro m the previous
layer. The whole MLP implementation is shown in Fig. 2.The network main ly consists of input registers, control unit,
neurons and output register. To provide on neuron output to the next stage at each clock cycle a Multiplexer and a counter is
used. The training of the network is done in software and the results loaded into hardware .Weights are updated during the
training process, but remain constant during the detection process. The Register Transfer Level design of the system has
been carried out using standard VHDL as the hardware description language. This language allows three different levels of
description. We have chosen RTL to imp lement this system. The entire design process was done using the ISE development
tool, fro m Xilin x (ISE development). The system physically imp lemented on Spartan -3 XC3 S4000 XILINX FPGA device.
Issn 2250-3005(online) December| 2012 Page 23
4. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8
Fig2.The RTL bl ock di agram of MLP neural network
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5. Results:
Fig 3. Inner layer top view of neural network.
Fig 4. Inner layer RTL Schematic of Neural network.
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Fig 5 . Inner layer Design summary
Fig 6. Input layer’s top view.
Fig 7.Input layer network of neural network.
Fig 8. Input layer Design summary.
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6. I nternational Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 8
Fig 9. Simulat ion Results for Input layer of neural netwo rk
Fig 10. Sig mo id layer RTL Schematic of Neural network
Fig 11.Sig moid layer Design summary
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6. COMPARIS ION RES ULTS
The device used to take Co mparis on results is Spartan XC3S400-5 PQ208.
Tab 1: Co mparison results of Sig moid function proposed system and previous system.
7. CONCLUS ION
It is seen from the comparison table in section VI that previous system [15] used more number of Slices, LUT’s and
IOB’s for sigmoid function. So our sigmoid function system used less number of resources as mentioned above. So we have
effectively reduced the area utilization of these neural network systems. This has increased compactness & reliability of our
system. In future our system permits us to achieve maximu m level of optimization.Therefore one should aim at giving a
hardware platform for ANN like FPGA because of the re-configurability of FPGA, we can develop the prototypes of
hardware based ANNs very easily. Mapping FPGA into Field programmab le Neural Network Arrays can fin d vast
applications in real time analysis .
REFERENCES
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