2. PrISMA: FPGAs central role
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The document discusses the Protein Identification through String Matching Algoritm (PrISMA) project. PrISMA aims to improve the efficiency of proteomic biomarker recognition and maintain application versatility for different databases and biomarkers. It proposes using FPGAs, which offer more versatility than ASICs but better energy efficiency than general purpose processors. Next steps include defining the ideal number of hardware cores, handling the complete human protein database across devices, optimizing communication and results collection, and implementing communication between multiple ZedBoards.
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Author: ADARSH KUMAR B, BIBIN CHRISTOPHER, ISSAC SAJAN, AJ DEEPA
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
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Website: https://www.logic-fruit.com/
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Embedded system software is highly constrained from performance, memory footprint, energy consumption
and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC).
Instruction cache has major contribu
tion in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as well. Hence, it is required to
maintain a trade
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off between cache sizes and performance improvement offered.
Determining the number
of cache lines and size of cache line are important parameters for cache designing. The design space for
cache is quite large. It is time taking to execute the given application with different cache sizes on an
instruction set simula
tor (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to
identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or
nearly best IPC. Cache size is derived, at a higher abstract
ion level, from basic block analysis in the Low
Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross
validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out
-
of
-
order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or
estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache
line size, number of cache lines).
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Author: ADARSH KUMAR B, BIBIN CHRISTOPHER, ISSAC SAJAN, AJ DEEPA
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Achieve High-Performance with Optimizing Device Specifications in FPGA Design
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Field-Programmable Gate Arrays (FPGAs) are becoming an interesting alternative for next-generation High-Performance Computing (HPC) systems, with changeable fabrics delivering increased performance over time.
To know more about, how to
Achieve High-Performance with optimizing Device Specifications in FPGA design, see https://www.logic-fruit.com/blog/fpga/high-performance-in-fpga-design/
About Logic Fruit Technologies
Logic Fruit Technologies is a product engineering R&D & consulting services provider for embedded systems and application development. We provide end-to-end solutions from the conception of the idea and design to the finished product. We have been servicing customers globally for over a decade.
The company has specific experience in various fields, such as
-FPGA Design & hardware design
RTL IP Design
A variety of digital protocols
Communication buses as1G, 10G Ethernet
PCIe
DIGRF
STM16/64
HDMI.
Logic Fruit Technologies is also an expert in developing,
software-defined radio (SDR) IPs
Encryption
Signal generation
Data analysis, and
Multiple Image Processing Techniques.
Recently Logic Fruit technologies are also exploring FPGA acceleration on data centers for real-time data processing.
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#LFT #LogicFruitTechnologies #LogicFruit
Interested to view more SlideShares, Click on the below links,
https://www.slideshare.net/LogicFruit/a-designers-practical-guide-to-arinc-429-standard-3pptx
https://www.slideshare.net/LogicFruit/a-swift-introduction-to-milstd
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https://www.slideshare.net/LogicFruit/arinc-629-digital-data-bus-specifications/LogicFruit/arinc-629-digital-data-bus-specifications
https://www.slideshare.net/LogicFruit/afdx
https://www.slideshare.net/LogicFruit/end-system-design-parameters-of-the-arinc-664-part-7
https://www.slideshare.net/LogicFruit/compute-express-link-cxl-everything-you-ought-to-know
https://www.logic-fruit.com/blog/fpga/what-is-fpga/
https://www.slideshare.net/LogicFruit/cxl-vs-pcie-gen-5-the-brief-comparison
https://www.slideshare.net/LogicFruit/fpga-technology-development-and-market-trends-in-the-new-decade
https://www.slideshare.net/LogicFruit/fpga-design-an-ultimate-guide-for-fpga-enthusiasts
https://www.slideshare.net/LogicFruit/fpga-vs-asic-design-comparison
https://www.slideshare.net/LogicFruit/afdx-a-timedeterministic-application-of-arinc-664-part-7
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https://www.slideshare.net/LogicFruit/arinc-8182-standaA Dynamic Programming Approach to Energy-Efficient Scheduling on Multi-FPGA b...
This paper has been studied an important issue of energy-efficient scheduling on multi-FPGA systems. The main challenges are integral allocation, reconfiguration overhead and exclusiveness and energy minimization with deadline constraint. To tackle these challenges, based on the theory of dynamic programming, we have designed and implemented an energy-efficient scheduling on multi-FPGA systems. Differently, we have presented a MLPF algorithm for task placement on FPGAs. Finally, the experimental results have demonstrated that the proposed algorithm can successfully accommodate all tasks without violation of the deadline constraint. Additionally, it gains higher energy reduction 13.3% and 26.3% than that of Particle Swarm Optimization and fully balanced algorithm, respectively.
Darren Jiao_resume
Deyu Jiao is pursuing a Master's degree in Electrical and Computer Engineering from Duke University with a 3.9 GPA. He received a Bachelor's degree in Information Engineering from Southeast University in China with a 3.58 GPA. His work experience includes an internship at the Institute of RF- & OE-ICS in China where he designed a two-stage op-amp that exceeded specifications. His graduate projects at Duke include developing an un-pipelined single-cycle processor using VHDL and a write-back cache on an FPGA that optimized clock frequency and hit latency.
Reconfigurable Embedded Systems Applications for Versatile Biomedical Measure...
Reconfigurable Embedded Systems Applications for Versatile Biomedical Measure...NECST Lab @ Politecnico di Milano
The document proposes a low-power, modular, and versatile device for biomedical measurements using an FPGA-based multi-processor system-on-chip. Case studies demonstrating EKG monitoring, pupil dilation tracking, and heterogeneous signal processing are presented. The conclusions state that FPGA systems are well-suited for applications requiring low power consumption, multi-purpose capabilities, extended connectivity, and high performance parallel processing.Fpaa 1
This document discusses field-programmable analog arrays (FPAAs). FPAAs allow for faster prototyping and time-to-market by making analog circuits programmable. They contain configurable analog blocks (CABs) that can implement functions like amplification, integration, and multiplication. CABs are arranged in a routing architecture that connects the blocks. Common CAB implementations are switched capacitor designs using operational amplifiers and passive elements. FPAAs have applications in signal processing and are provided by companies like Anadigm and Zetex Semiconductors. While promising, more development is still needed for programmable analog compared to digital FPGAs.
A novel mrp so c processor for dispatch time curtailment
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
[EWiLi2016] Towards a performance-aware power capping orchestrator for the Xe...
In the last few years, multi-core processors entered into the domain of embedded systems: this, together with virtualization techniques, allows multiple applications to easily run on the same System-on-Chip (SoC). As power consumption remains one of the most impacting costs on any digital system, several approaches have been explored in literature to cope with power caps, trying to maximize the performance of the hosted applications. In this paper, we present some preliminary results and opportunities towards a performance-aware power capping orchestrator for the Xen hypervisor. The proposed solution, called XeMPUPiL, uses the Intel Running Average Power Limit (RAPL) hardware interface to set a strict limit on the processor’s power consumption, while a software-level Observe-Decide-Act (ODA) loop performs an exploration of the available resource allocations to find the most power efficient one for the running workload. We show how XeMPUPiL is able to achieve higher performance under different power caps for almost all the different classes of benchmarks analyzed (e.g., CPU-, memory- and IO-bound).
Full paper: http://ceur-ws.org/Vol-1697/EWiLi16_17.pdf
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
Embedded system software is highly constrained from performance, memory footprint, energy consumption and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC). Instruction cache has major contribution in improving IPC. Cache memories are realized on the same chip where the processor is running. This considerably increases the system cost as well. Hence, it is required to maintain a trade-off between cache sizes and performance improvement offered. Determining the number of cache lines and size of cache line are important parameters for cache designing. The design space for cache is quite large. It is time taking to execute the given application with different cache sizes on an instruction set simulator (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or nearly best IPC. Cache size is derived, at a higher abstraction level, from basic block analysis in the Low Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out-of-order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache line size, number of cache lines).
Dominant block guided optimal cache size estimation to maximize ipc of embedd...
Embedded system software is highly constrained from performance, memory footprint, energy consumption
and implementing cost view point. It is always desirable to obtain better Instructions per Cycle (IPC).
Instruction cache has major contribu
tion in improving IPC. Cache memories are realized on the same chip
where the processor is running. This considerably increases the system cost as well. Hence, it is required to
maintain a trade
-
off between cache sizes and performance improvement offered.
Determining the number
of cache lines and size of cache line are important parameters for cache designing. The design space for
cache is quite large. It is time taking to execute the given application with different cache sizes on an
instruction set simula
tor (ISS) to figure out the optimal cache size. In this paper, a technique is proposed to
identify a number of cache lines and cache line size for the L1 instruction cache that will offer best or
nearly best IPC. Cache size is derived, at a higher abstract
ion level, from basic block analysis in the Low
Level Virtual Machine (LLVM) environment. The cache size estimated from the LLVM environment is cross
validated by simulating the set of benchmark applications with different cache sizes in SimpleScalar’s out
-
of
-
order simulator. The proposed method seems to be superior in terms of estimation accuracy and/or
estimation time as compared to the existing methods for estimation of optimal cache size parameters (cache
line size, number of cache lines).
Shantanu's Resume
Shantanu Telharkar is pursuing a Master of Science in Electrical Engineering from San Jose State University with a specialization in digital systems and logic design. He has relevant experience as an FPGA systems design intern at MathWorks and as an FPGA design intern at Atria Logic and Interface Design Associates Private Limited. His technical skills include programming languages like C, C++, Verilog, VHDL, and SystemVerilog as well as tools like Xilinx Vivado, MATLAB, and SIMULINK.
Particle swarm optimization based clustering by preventing residual nodes in ...
Particle swarm optimization based clustering by preventing residual nodes in ...LogicMindtech Nologies
NS2 Projects for M. Tech, NS2 Projects in Vijayanagar, NS2 Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, NS2 IEEE projects in Bangalore, IEEE 2015 NS2 Projects, WSN and MANET Projects, WSN and MANET Projects in Bangalore, WSN and MANET Projects in VijayangarBlastFunction: How to combine Serverless and FPGAs
BlastFunction is a serverless platform that brings FPGA acceleration capabilities for specific functions through heterogeneous computing. It enables resource sharing across multiple users to maximize FPGA utilization and minimize costs for cloud providers. BlastFunction manages functions and machines, redistributing workloads across nodes equipped with FPGAs. Initial results show BlastFunction improved FPGA utilization and increased performance per watt for benchmark applications compared to native CPU execution. Future work includes porting BlastFunction to AWS and automating cluster management.
Oindree basu resume
Oindree Basu is pursuing a Master's degree in Electrical and Computer Engineering at Purdue University, having previously earned a Bachelor's degree in Electronics and Communication Engineering from National Institute of Technology Durgapur in India. The document provides details of Basu's education background, research experience including projects on digital circuit design and FPGA implementation of neural networks, as well as technical skills in hardware description languages, software tools, and programming languages.
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2. PrISMA: FPGAs central role
- 1. Protein Identification through String Matching Algoritm Gea Bianchi –Fabiola Casasopra {gea.bianchi, fabiola.casasopra}@mail.polimi.it NECSTLab, March 2016
- 2. 2 PrISMA has two main goals: Improving the efficiency of the proteomic biomarkers recognition Mantain the versatility of the application: different databases and different protein biomarkers “A goal is a dream with a deadline” -N. Hill
- 3. 3 ASIC HW only implementation FPGA HW/SW co-design GPP SW only implementation Max versatility Max energy efficiency FPGAs are: They are between GPPs and ASICs in terms of versatility and energy efficiency Reprogrammable and less time-demanding then ASICs GPP = General Purpose Processor; FPGA = Field Programmable Gate Array; ASIC = Application-specific Integrated Circuit. MAX EASE OF PROGRAMMING
- 4. 4 Define the ideal number of HW cores (in terms of parallelization and energy efficiency) Handle the complete human protein database and share it among the devices Optimize the comunication Optimize the results collection Implement the comunication between more AVNET ZedBoardsTM Next steps to give life to PrISMA …
- 5. Protein Identification through String Matching Algoritm Thanks for your attention! Gea Bianchi –Fabiola Casasopra {gea.bianchi, fabiola.casasopra}@mail.polimi.it facebook.com/PrISMA2016 twitter.com/PrISMA_NECSTLab Channel: NECST@XOHW16