This document discusses implementing a cryptographic hash function (MD5) on an FPGA-based embedded system. It provides an overview of FPGA-based embedded systems and FPGAs, describing how FPGAs can be programmed to perform digital logic functions. It also outlines the MD5 hash algorithm and describes plans to implement it in hardware on an FPGA to potentially increase processing speed through parallelization. The goal is to explore hardware implementation of cryptographic hash functions on FPGA-based embedded systems.
Xilinx vs Intel (Altera) FPGA performance comparison Roy Messinger
You're welcome to check out this interesting comparison I've conducted between these 2 vendors. Very interesting and surprising results (I did not expect such differences).
The Microarchitecure Of FPGA Based Soft ProcessorDeepak Tomar
this presentation is on the Paper "The Microarchitecure Of FPGA Based Soft Processor" by Peter Yiannacouras, Jonathan Rose and
J Gregory Steffan
Dept. of Electrical and Computer Engineering
University of Toronto
The document describes using an IP core in a Xilinx FPGA design. Specifically, it discusses:
1) Creating an adder/subtractor IP core using the Xilinx CORE Generator.
2) Connecting the IP core as a component in a top-level VHDL file.
3) Synthesizing and programming the design onto a Spartan 3E FPGA board to test the four-bit adder/subtractor functionality.
This document appears to be a term paper on developing a telephone directory system using C language. It includes sections on introduction, system requirements, system description, system design, source code, output, testing, and future scope. The source code section includes the code to create a linked list structure to store contact entries, add new entries, display single and all entries, delete entries, save and load entries from a file, and delete all files. It implements a basic menu driven telephone directory management system.
The document discusses using Impulse C to program FPGAs for financial applications. Key points:
1) Impulse C allows developing FPGA accelerators using standard C/C++ and converts code into hardware modules, interfaces and accelerators.
2) It supports parallel programming with multiple communicating hardware processes. Processes can interact through shared memory, streams or signals.
3) Popular FPGA configurations include hardware modules, embedded CPU cores, and host CPU accelerators. Configurations can be combined for specific applications.
This document summarizes an FPGA-based emulation platform called Makinote developed by BSC for prototyping large-scale RISC-V designs. It discusses the Makinote hardware platform consisting of an FPGA cluster with over 100 FPGAs and software tools. The tools include an FPGA shell for design implementation, integration with OpenPiton, and design partitioning across multiple FPGAs. Design verification methods using the FPGA platform aim to speed up verification by 3 orders of magnitude compared to simulation. Makinote is intended as an open platform for research collaboration in contrast to closed commercial emulation systems.
Xilinx vs Intel (Altera) FPGA performance comparison Roy Messinger
You're welcome to check out this interesting comparison I've conducted between these 2 vendors. Very interesting and surprising results (I did not expect such differences).
The Microarchitecure Of FPGA Based Soft ProcessorDeepak Tomar
this presentation is on the Paper "The Microarchitecure Of FPGA Based Soft Processor" by Peter Yiannacouras, Jonathan Rose and
J Gregory Steffan
Dept. of Electrical and Computer Engineering
University of Toronto
The document describes using an IP core in a Xilinx FPGA design. Specifically, it discusses:
1) Creating an adder/subtractor IP core using the Xilinx CORE Generator.
2) Connecting the IP core as a component in a top-level VHDL file.
3) Synthesizing and programming the design onto a Spartan 3E FPGA board to test the four-bit adder/subtractor functionality.
This document appears to be a term paper on developing a telephone directory system using C language. It includes sections on introduction, system requirements, system description, system design, source code, output, testing, and future scope. The source code section includes the code to create a linked list structure to store contact entries, add new entries, display single and all entries, delete entries, save and load entries from a file, and delete all files. It implements a basic menu driven telephone directory management system.
The document discusses using Impulse C to program FPGAs for financial applications. Key points:
1) Impulse C allows developing FPGA accelerators using standard C/C++ and converts code into hardware modules, interfaces and accelerators.
2) It supports parallel programming with multiple communicating hardware processes. Processes can interact through shared memory, streams or signals.
3) Popular FPGA configurations include hardware modules, embedded CPU cores, and host CPU accelerators. Configurations can be combined for specific applications.
This document summarizes an FPGA-based emulation platform called Makinote developed by BSC for prototyping large-scale RISC-V designs. It discusses the Makinote hardware platform consisting of an FPGA cluster with over 100 FPGAs and software tools. The tools include an FPGA shell for design implementation, integration with OpenPiton, and design partitioning across multiple FPGAs. Design verification methods using the FPGA platform aim to speed up verification by 3 orders of magnitude compared to simulation. Makinote is intended as an open platform for research collaboration in contrast to closed commercial emulation systems.
The document discusses FPGA design flow and programming. It describes the roles of the systems architect who defines high-level requirements and provides a golden model and test vectors. The FPGA designer is responsible for delivering a firmware that approximates the golden model on a hardware platform using vendor tools. The design flow includes simulation, synthesis, placement and routing, and testing at different stages to verify functionality and timing.
Implementation of Soft-core Processor on FPGADeepak Kumar
We can add a soft-core processor to a FPGA-based system after it's already designed. However, adding a hard-core processor requires either a different FPGA, or an additional chip on the board.
Accelerating Real Time Analytics with Spark Streaming and FPGAaaS with Prabha...Databricks
As Real Time Analytics built on Spark Streaming framework gains increasing adoption and deployment in the data center, the need for hardware accelerators to scale for performance and offer lower latency for certain applications is becoming increasingly apparent. Recently reconfigurable accelerators based on FPGAs (Field Programmable Gate Arrays) have been proposed to accelerate the analytics workloads with support for low-latency streaming. This is becoming a feasible and attractive option now as major CSPs (Cloud Service Providers) announce deployment of FPGAs- as-a-Service (FPGAaaS).
However there are certain challenges in using FPGA accelerators in the cloud that first must be overcome: (1) Development of FPGA accelerators is challenging because of the lack of mature high level language support and standard accelerator interfaces; (2) Using hardware accelerators from high level application frameworks like Spark require special consideration for the efficient transfer of data to and from the application; and (3) Managing the FPGA accelerators for sharing across different applications is limited by the lack of a common resource sharing paradigm.
In this session, we’ll present a runtime framework that addresses these challenges. The framework will present highly optimized FPGA based accelerated functions as a service (AFaaS) to the application. The framework will demonstrate seamless integration of accelerators into existing Big Data Frameworks using Java, Scala, Pyton or other high level language APIs. The framework will also support the transfer of streaming data to/from the FPGA to the application and the sharing of accelerator libraries across different applications. Key takeaways:
This paper demonstrates the feasibility of accelerating Real Time Analytics applications using Spark Streaming with accelerated functions based on FPGAaaS to deliver higher performance efficiencies at lower latencies.
The paper will provide performance data for Spark Streaming applications using our solution compared to current native software implementations.
The document discusses FPGA based system design including the role of FPGAs in digital design, FPGA types, FPGA architectures, and the advantages of FPGAs over custom VLSI. FPGAs can be programmed and reprogrammed, allowing designs to be tested immediately without waiting for a finished chip. This makes FPGAs well-suited for prototyping. SRAM-based FPGAs are the most common type and can be reprogrammed in the field. FPGAs include programmable logic blocks and interconnects that can implement multi-level logic functions.
Using a Field Programmable Gate Array to Accelerate Application PerformanceOdinot Stanislas
Intel s'intéresse tout particulièrement aux FPGA et notamment au potentiel qu'ils apportent lorsque les ISV et développeurs ont des besoins très spécifiques en Génomique, traitement d'images, traitement de bases de données, et même dans le Cloud. Dans ce document vous aurez l'occasion d'en savoir plus sur notre stratégie, et sur un programme de recherche lancé par Intel et Altera impliquant des Xeon E5 équipés... de FPGA
Intel is looking at FPGA and what they bring to ISVs and developers and their very specific needs in genomics, image processing, databases, and even in the cloud. In this document you will have the opportunity to learn more about our strategy, and a research program initiated by Intel and Altera involving Xeon E5 with... FPGA inside.
Auteur(s)/Author(s):
P. K. Gupta, Director of Cloud Platform Technology, Intel Corporation
Amazon EC2 F1 is a new compute instance with programmable hardware for application acceleration. With F1, you can directly access custom FPGA hardware on the instance in a few clicks.
Learning Objectives:
• Learn about the capabilities, features, and benefits of the new F1 instances
• Develop your FPGA using the F1 Hardware Developer Kit and FPGA Developer AMI
• Deploy your FPGA acceleration code using F1 instances
• Use F1 instances for hardware acceleration in your applications
• Learn how to offer pre-packaged Amazon FPGA Machine Images (AFIs) to your customers through the AWS Marketplace
Ablaze Wireless used S2C's Dual Stratix-4 820 FPGA prototyping platform to develop their femtocell baseband processor, finding that the larger FPGA capacity and faster image downloading with Prodigy Player Pro software accelerated their development cycle by 3-6 months compared to their previous platform. The S2C solution enabled Ablaze Wireless to prototype more of their system-on-chip design on FPGAs at once and easily adjust clock frequencies without needing new FPGA images. Ablaze Wireless was pleased with the powerful capabilities and design advantages provided by S2C's rapid prototyping solutions.
This document provides an introduction to FPGA and SOPC development boards. It discusses the architecture of programmable logic devices including PLDs, CPLDs, and FPGAs. Examples are given of Altera MAX7000 CPLD and Stratix series FPGA architectures. The benefits of FPGAs are outlined compared to ASICs. The document then reviews the FPGA design flow and different design entry methods like VHDL and block diagrams. It provides examples of the Altera Stratix Nios development board and UP2 development board. Finally, it introduces the Altera Quartus II design software used for FPGA development.
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang Spark Summit
In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.
The presentation discusses FPGAs and their use in automation systems. FPGAs provide benefits like reliability, determinism, parallelism, and reconfigurability. National Instruments offers LabVIEW software and CompactRIO hardware to program FPGAs for applications such as fast control, sensor processing, triggering, and data acquisition. The CompactRIO architecture uses an FPGA for timing-critical tasks and a real-time processor for control, analysis, and communication. LabVIEW provides graphical programming for both.
This document provides an introduction to electronic design automation (EDA) tools and discusses different types of programmable logic devices including field programmable gate arrays (FPGAs) and complex programmable logic devices (CPLDs). It describes the basic architecture of FPGAs including logic blocks, interconnects, and input/output blocks. The advantages of FPGAs such as shorter development time and flexibility are also summarized.
FPGA Selection Methodology for Real time projectsKrishna Gaihre
- The document discusses various factors to consider when selecting an FPGA chip for a project, such as resource utilization, IO count, frequency, power, cost, package size, available IP cores, vendor support, and EDA tool support.
- It outlines the FPGA offerings from major vendors Xilinx and Altera/Intel, including families like Zynq, Artix, Virtex, Stratix and Cyclone that are suitable for different applications and performance levels.
- Key trends in the FPGA market include growing use in embedded systems, computer vision, high-speed networks, and other domains, with devices integrating both programmable logic and processor systems.
This document proposes a platform for networked FPGA systems using an ORB (Object Request Broker) engine with a Java-to-HDL synthesizer called JavaRock. This allows software engineers to easily use FPGAs. As a demonstration, a remotely controlled FPGA system for an inverted pendulum was designed. Measurements found the ORB engine latency to be below 200 microseconds with a hardware resource overhead of 372 slices on a Spartan6 FPGA. Future work aims to improve performance and build an ecosystem to support networked and distributed FPGA system design using the ORB architecture.
This document provides information on choosing processors and development tools for embedded applications. It discusses different types of processors like microcontrollers, microprocessors, DSPs and FPGAs. It also covers topics like multicore processors, embedded software design flow, hardware design flow, processor selection criteria, embedded development life cycle and more. The goal is to help readers understand the various options available when selecting hardware and tools for their embedded projects.
This document discusses emulating systems-on-chip (SoCs) on Amazon Web Services (AWS) field-programmable gate arrays (FPGAs). It describes how the author validated a RISC-V CPU design by running Linux on it within an AWS F1 FPGA instance. It provides details on using Bluespec System Verilog (BSV) to model CPU cores, the AWS FPGA shell, Connectal for connecting hardware and software, and virtio device models to emulate I/O without custom drivers. The document shows how the approach allows running different RISC-V processor designs from the DARPA SSITH program securely in the cloud for security evaluation.
Transparent GPU Exploitation on Apache Spark with Kazuaki Ishizaki and Madhus...Databricks
Graphics Processing Units (GPUs) are becoming popular for achieving high performance of computation intensive workloads. The GPU offers thousands of cores for floating point computation. This is beneficial to machine learning algorithms that are computation intensive and are parallelizable on the Spark platform. While the current execution strategy of Spark is to execute computations for the workload across nodes, only CPUs on each node execute computation.
If Spark could use GPUs on each node, users benefit from GPUs that can reduce the execution time of the algorithm and reduce the number of nodes in a cluster. Recently, while application programmers use DataFrame APIs for their application, machine learning algorithms work with RDDs that keep data across nodes for distributed computation on CPU cores. A RDD keeps data as a Scala collection class in a row-based format. The computation model of GPU can achieve high performance for contiguous data in a column-based format. For enabling efficient GPU computation on Spark, we present a column-based RDD that can keep data as an array. When we execute them on the GPU, our implementation simply copies data in the column-based RDD to the GPU device memory. Then, each GPU cores execute operations faster on the device memory. CPU cores can execute existing functions on the column-based RDD.
In this session, we will give the following contribution to the Spark community:
(1) we give a brief design overview of transparent GPU exploitations from programmers
(2) we show our APIs to build a GPU-accelerated library using column-based RDD and show the performance gain of some programs
(3) we discuss current work for transparent GPU code generation from DataFrame APIs
The package for (2) is available at http://github.com/IBMSparkGPU/GPUEnabler
This presentation is dedicated to Field Programmable Gate Array (FPGA), its interaction with CPU, distrbuted resources, use of FPGA for prototyping chips, and OpenCL in FPGA.
This presentation by Andriy Smolskyy, GlobalLogic Engineering Consultant, was delivered at a GlobalLogic Embedded TechTalk in Lviv on March 29, 2017.
The document describes an IBM workshop on CAPI and OpenCAPI technologies. It provides an overview of FPGA acceleration using SNAP, including how SNAP simplifies FPGA programming using a C/C++ based approach. Examples of use cases for FPGA acceleration like video processing and machine learning inference are also presented.
Part 4 Maximizing the utilization of GPU resources on-premise and in the cloudUniva, an Altair Company
This document discusses scheduling GPUs on premise and in the cloud with Grid Engine. It covers challenges in using GPUs with Grid Engine, how applications interact with GPUs, configuring metadata for GPUs in Grid Engine, GPU and CPU binding, managing environments and containers for GPUs, accounting for GPU usage in Grid Engine, and an example workflow for setting everything up. It also previews upcoming improvements in Grid Engine for better support of GPUs like the A100 and MIGs.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
The document discusses FPGA design flow and programming. It describes the roles of the systems architect who defines high-level requirements and provides a golden model and test vectors. The FPGA designer is responsible for delivering a firmware that approximates the golden model on a hardware platform using vendor tools. The design flow includes simulation, synthesis, placement and routing, and testing at different stages to verify functionality and timing.
Implementation of Soft-core Processor on FPGADeepak Kumar
We can add a soft-core processor to a FPGA-based system after it's already designed. However, adding a hard-core processor requires either a different FPGA, or an additional chip on the board.
Accelerating Real Time Analytics with Spark Streaming and FPGAaaS with Prabha...Databricks
As Real Time Analytics built on Spark Streaming framework gains increasing adoption and deployment in the data center, the need for hardware accelerators to scale for performance and offer lower latency for certain applications is becoming increasingly apparent. Recently reconfigurable accelerators based on FPGAs (Field Programmable Gate Arrays) have been proposed to accelerate the analytics workloads with support for low-latency streaming. This is becoming a feasible and attractive option now as major CSPs (Cloud Service Providers) announce deployment of FPGAs- as-a-Service (FPGAaaS).
However there are certain challenges in using FPGA accelerators in the cloud that first must be overcome: (1) Development of FPGA accelerators is challenging because of the lack of mature high level language support and standard accelerator interfaces; (2) Using hardware accelerators from high level application frameworks like Spark require special consideration for the efficient transfer of data to and from the application; and (3) Managing the FPGA accelerators for sharing across different applications is limited by the lack of a common resource sharing paradigm.
In this session, we’ll present a runtime framework that addresses these challenges. The framework will present highly optimized FPGA based accelerated functions as a service (AFaaS) to the application. The framework will demonstrate seamless integration of accelerators into existing Big Data Frameworks using Java, Scala, Pyton or other high level language APIs. The framework will also support the transfer of streaming data to/from the FPGA to the application and the sharing of accelerator libraries across different applications. Key takeaways:
This paper demonstrates the feasibility of accelerating Real Time Analytics applications using Spark Streaming with accelerated functions based on FPGAaaS to deliver higher performance efficiencies at lower latencies.
The paper will provide performance data for Spark Streaming applications using our solution compared to current native software implementations.
The document discusses FPGA based system design including the role of FPGAs in digital design, FPGA types, FPGA architectures, and the advantages of FPGAs over custom VLSI. FPGAs can be programmed and reprogrammed, allowing designs to be tested immediately without waiting for a finished chip. This makes FPGAs well-suited for prototyping. SRAM-based FPGAs are the most common type and can be reprogrammed in the field. FPGAs include programmable logic blocks and interconnects that can implement multi-level logic functions.
Using a Field Programmable Gate Array to Accelerate Application PerformanceOdinot Stanislas
Intel s'intéresse tout particulièrement aux FPGA et notamment au potentiel qu'ils apportent lorsque les ISV et développeurs ont des besoins très spécifiques en Génomique, traitement d'images, traitement de bases de données, et même dans le Cloud. Dans ce document vous aurez l'occasion d'en savoir plus sur notre stratégie, et sur un programme de recherche lancé par Intel et Altera impliquant des Xeon E5 équipés... de FPGA
Intel is looking at FPGA and what they bring to ISVs and developers and their very specific needs in genomics, image processing, databases, and even in the cloud. In this document you will have the opportunity to learn more about our strategy, and a research program initiated by Intel and Altera involving Xeon E5 with... FPGA inside.
Auteur(s)/Author(s):
P. K. Gupta, Director of Cloud Platform Technology, Intel Corporation
Amazon EC2 F1 is a new compute instance with programmable hardware for application acceleration. With F1, you can directly access custom FPGA hardware on the instance in a few clicks.
Learning Objectives:
• Learn about the capabilities, features, and benefits of the new F1 instances
• Develop your FPGA using the F1 Hardware Developer Kit and FPGA Developer AMI
• Deploy your FPGA acceleration code using F1 instances
• Use F1 instances for hardware acceleration in your applications
• Learn how to offer pre-packaged Amazon FPGA Machine Images (AFIs) to your customers through the AWS Marketplace
Ablaze Wireless used S2C's Dual Stratix-4 820 FPGA prototyping platform to develop their femtocell baseband processor, finding that the larger FPGA capacity and faster image downloading with Prodigy Player Pro software accelerated their development cycle by 3-6 months compared to their previous platform. The S2C solution enabled Ablaze Wireless to prototype more of their system-on-chip design on FPGAs at once and easily adjust clock frequencies without needing new FPGA images. Ablaze Wireless was pleased with the powerful capabilities and design advantages provided by S2C's rapid prototyping solutions.
This document provides an introduction to FPGA and SOPC development boards. It discusses the architecture of programmable logic devices including PLDs, CPLDs, and FPGAs. Examples are given of Altera MAX7000 CPLD and Stratix series FPGA architectures. The benefits of FPGAs are outlined compared to ASICs. The document then reviews the FPGA design flow and different design entry methods like VHDL and block diagrams. It provides examples of the Altera Stratix Nios development board and UP2 development board. Finally, it introduces the Altera Quartus II design software used for FPGA development.
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang Spark Summit
In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.
The presentation discusses FPGAs and their use in automation systems. FPGAs provide benefits like reliability, determinism, parallelism, and reconfigurability. National Instruments offers LabVIEW software and CompactRIO hardware to program FPGAs for applications such as fast control, sensor processing, triggering, and data acquisition. The CompactRIO architecture uses an FPGA for timing-critical tasks and a real-time processor for control, analysis, and communication. LabVIEW provides graphical programming for both.
This document provides an introduction to electronic design automation (EDA) tools and discusses different types of programmable logic devices including field programmable gate arrays (FPGAs) and complex programmable logic devices (CPLDs). It describes the basic architecture of FPGAs including logic blocks, interconnects, and input/output blocks. The advantages of FPGAs such as shorter development time and flexibility are also summarized.
FPGA Selection Methodology for Real time projectsKrishna Gaihre
- The document discusses various factors to consider when selecting an FPGA chip for a project, such as resource utilization, IO count, frequency, power, cost, package size, available IP cores, vendor support, and EDA tool support.
- It outlines the FPGA offerings from major vendors Xilinx and Altera/Intel, including families like Zynq, Artix, Virtex, Stratix and Cyclone that are suitable for different applications and performance levels.
- Key trends in the FPGA market include growing use in embedded systems, computer vision, high-speed networks, and other domains, with devices integrating both programmable logic and processor systems.
This document proposes a platform for networked FPGA systems using an ORB (Object Request Broker) engine with a Java-to-HDL synthesizer called JavaRock. This allows software engineers to easily use FPGAs. As a demonstration, a remotely controlled FPGA system for an inverted pendulum was designed. Measurements found the ORB engine latency to be below 200 microseconds with a hardware resource overhead of 372 slices on a Spartan6 FPGA. Future work aims to improve performance and build an ecosystem to support networked and distributed FPGA system design using the ORB architecture.
This document provides information on choosing processors and development tools for embedded applications. It discusses different types of processors like microcontrollers, microprocessors, DSPs and FPGAs. It also covers topics like multicore processors, embedded software design flow, hardware design flow, processor selection criteria, embedded development life cycle and more. The goal is to help readers understand the various options available when selecting hardware and tools for their embedded projects.
This document discusses emulating systems-on-chip (SoCs) on Amazon Web Services (AWS) field-programmable gate arrays (FPGAs). It describes how the author validated a RISC-V CPU design by running Linux on it within an AWS F1 FPGA instance. It provides details on using Bluespec System Verilog (BSV) to model CPU cores, the AWS FPGA shell, Connectal for connecting hardware and software, and virtio device models to emulate I/O without custom drivers. The document shows how the approach allows running different RISC-V processor designs from the DARPA SSITH program securely in the cloud for security evaluation.
Transparent GPU Exploitation on Apache Spark with Kazuaki Ishizaki and Madhus...Databricks
Graphics Processing Units (GPUs) are becoming popular for achieving high performance of computation intensive workloads. The GPU offers thousands of cores for floating point computation. This is beneficial to machine learning algorithms that are computation intensive and are parallelizable on the Spark platform. While the current execution strategy of Spark is to execute computations for the workload across nodes, only CPUs on each node execute computation.
If Spark could use GPUs on each node, users benefit from GPUs that can reduce the execution time of the algorithm and reduce the number of nodes in a cluster. Recently, while application programmers use DataFrame APIs for their application, machine learning algorithms work with RDDs that keep data across nodes for distributed computation on CPU cores. A RDD keeps data as a Scala collection class in a row-based format. The computation model of GPU can achieve high performance for contiguous data in a column-based format. For enabling efficient GPU computation on Spark, we present a column-based RDD that can keep data as an array. When we execute them on the GPU, our implementation simply copies data in the column-based RDD to the GPU device memory. Then, each GPU cores execute operations faster on the device memory. CPU cores can execute existing functions on the column-based RDD.
In this session, we will give the following contribution to the Spark community:
(1) we give a brief design overview of transparent GPU exploitations from programmers
(2) we show our APIs to build a GPU-accelerated library using column-based RDD and show the performance gain of some programs
(3) we discuss current work for transparent GPU code generation from DataFrame APIs
The package for (2) is available at http://github.com/IBMSparkGPU/GPUEnabler
This presentation is dedicated to Field Programmable Gate Array (FPGA), its interaction with CPU, distrbuted resources, use of FPGA for prototyping chips, and OpenCL in FPGA.
This presentation by Andriy Smolskyy, GlobalLogic Engineering Consultant, was delivered at a GlobalLogic Embedded TechTalk in Lviv on March 29, 2017.
The document describes an IBM workshop on CAPI and OpenCAPI technologies. It provides an overview of FPGA acceleration using SNAP, including how SNAP simplifies FPGA programming using a C/C++ based approach. Examples of use cases for FPGA acceleration like video processing and machine learning inference are also presented.
Part 4 Maximizing the utilization of GPU resources on-premise and in the cloudUniva, an Altair Company
This document discusses scheduling GPUs on premise and in the cloud with Grid Engine. It covers challenges in using GPUs with Grid Engine, how applications interact with GPUs, configuring metadata for GPUs in Grid Engine, GPU and CPU binding, managing environments and containers for GPUs, accounting for GPU usage in Grid Engine, and an example workflow for setting everything up. It also previews upcoming improvements in Grid Engine for better support of GPUs like the A100 and MIGs.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
10. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
11. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
12. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
13. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
14. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
15. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
16. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
17. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
What are FPGAs
• FPGAs are programmable digital logic
chip
• We can program them to do almost
any digital function
• Here’s the general flow of working with
FPGAs:
• We use a computer to describe a ”logic
function”that we want.We might draw
a schematic
• We compile the ”logic function”, using
a software provided by the FPGA
vendor
• That creates a binary file that can be
downloaded into the FPGA
• Binary file can be downloaded to the
FPGA by connecting cable
• That’s it! our FPGA behaves
according to our ”logic function”
Figur : Xilinx Spartan 3e Starter
FPGA Board
Figur : Altera Cyclone FPGA
Board
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
21. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
Features of Xilkernel
Key Features
• A POSIX API targeting embedded kernels
• Core kernel features such as:
• POSIX threads with round-robin or strict priority scheduling
• POSIX synchronization services - semaphores and mutex locks
• POSIX IPC services - message queues and shared memory
• Statically creating threads that startup with the kernel
• System call interface to the kernel
• Support for creating processes out of separate executable Executable
Link Files (ELF)
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
22. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
Features of Xilkernel
Key Features
• A POSIX API targeting embedded kernels
• Core kernel features such as:
• POSIX threads with round-robin or strict priority scheduling
• POSIX synchronization services - semaphores and mutex locks
• POSIX IPC services - message queues and shared memory
• Statically creating threads that startup with the kernel
• System call interface to the kernel
• Support for creating processes out of separate executable Executable
Link Files (ELF)
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
23. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
Features of Xilkernel
Key Features
• A POSIX API targeting embedded kernels
• Core kernel features such as:
• POSIX threads with round-robin or strict priority scheduling
• POSIX synchronization services - semaphores and mutex locks
• POSIX IPC services - message queues and shared memory
• Statically creating threads that startup with the kernel
• System call interface to the kernel
• Support for creating processes out of separate executable Executable
Link Files (ELF)
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
29. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
MD5 Hash Algorithm
Hash Function
A hash function is any algorithm or subroutine that maps data sets of
variable length to data sets of a fixed length.[2] Several hash functions are:
MD2, MD4, MD5, SHA
MD5
A widely used cryptographic hash function that produces 128-bit hash
value. It is used to check data integrity
Why MD5 ?
• quick hash value generation
• adaptability
• widely used secure hash algorithm particularly in Internet-standard
message authentication.[1]
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
45. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
Future Scope
Project direction will be implement our design on Zynq a new 28 nm
FPGA kit with Dual Cortex ARM hardcore processor, we will relatively
analysis our design on linux running on zynq with presepective Posix
programming and we also look into the activity by implementing Android
on zynq and measure the relative security isseues, if possible, we would like
to expand the security measurement from Hashing to digital signature
scheme to serve the purpose of authentication
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded
46. Implementation
of Crypto-
grahic
Hash
Function
on FPGA
based
Embedded
System
Anupam
Pandit,
Kumaresh
Chattopad-
hyay
Proma
Goswami
Embedded
System
Overview
FPGAs
Embedded
Design
using
FPGA
Handshaking
with FPGA
MD5 Hash
Algorithm
Flowchart
Hardware
implemen-
Conclusion
Message integrity is also has the same importance than that of
Cryptographic algorithms as we can remain sure just by encrypting the
message we need to also sure about with the fact that, we are receiving
message form authenticated person and the data is also remain unchanged.
MD5 is widely used algorithm and its also very popular due to it small size
and also secured though some attacks on MD5 has been reported but its
use is not reduces and it still has its use on SSL and IPSEC. Using our
design we tried to give an modern approach of executing such algorithm
on thread level by suing RTOS and its also be used by future researcher
who wants to execute in similar fashion. Though this application is in need
of large occupation of program memory and for that SDRAM has been
used but for complete FPGA embedded device this program works fine
and capable of hashing of large size data up to 7000 bits at present instant
and could be make it larger.
Anupam Pandit, Kumaresh Chattopadhyay Proma Goswami Implementation of Cryptograhic Hash Function on FPGA based Embedded