Fpga Overlay Processor For Deep Neural Networks

Download Fpga Overlay Processor For Deep Neural Networks full books in PDF, epub, and Kindle. Read online Fpga Overlay Processor For Deep Neural Networks ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

FPGA Overlay Processor for Deep Neural Networks

Author : yunxuan Yu
Publisher :
ISBN 13 :
Total Pages : 186 pages
Book Rating : 4.:/5 (122 download)

DOWNLOAD NOW!

Book Synopsis FPGA Overlay Processor for Deep Neural Networks by : yunxuan Yu

Download or read book FPGA Overlay Processor for Deep Neural Networks written by yunxuan Yu and published by . This book was released on 2020 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid advancement of Artificial intelligence (AI) is making our everyday life easier with smart assistants, automatic medical analyzer, bank plagiarism checkers and traffic predictions, etc. Deep learning algorithms, especially deep convolutional neuron networks (DCNNs), achieve top performance on AI tasks, but suffers from dense computational requirements, which calls for hardware acceleration. In this thesis we propose several architectures including compilation flow for general DCNN acceleration using FPGA platform. Starting from late 2015 we began to design customized accelerators for popular DCNNs such as VGG and YOLOv2. We reformulate the convolution computation by flattening it to large-scale matrix multiplication between feature maps and convolution kernels, which can be computed as inner product. With this formulation, the accelerators across all layers can be unified to enhance resource sharing, and maximize utilization of computing resources. We also quantized the network into 8bit with negligible accuracy loss to reduce memory footprint and computation resources. Different parallelism optimization strategies are explored for different networks. The VGG16 accelerator achieved 1.15x throughput under 1.5x lower frequency compared with state-of-the art designs. The YOLOv2 accelerator was commercialized and employed for real-time subway X-ray auto-hazard detection. Based on the experience we gained through customized accelerator designing, we designed a RTL compiler as an end-to-end solution to automatically generate RTL design for given CNN network and FPGA platform, which greatly reduced the human effort in developing a specific network accelerator. The compiler applies analytical performance models to optimize parameters for modules based on a handwritten template library, such that the overall throughput is maximized. Several levels of parallelism for convolution are explored, including inter feature-map, intra-kernel-set, input/output channel, etc. We also optimize architectures for block RAM and input buffers to speed up data flow. We tested our compiler on several well-known CNNs including AlexNet and VGGNet for different FPGA platforms. The resulting AlexNet is 113.69 GOPS on Xilinx VCU095 and 177.44 GOPS on VC707, and VGGNet is 226 GOPS on VCU095 under 100MHZ. These are 1.3x, 2.1x and 1.2x better than the best reported FPGA accelerators at that time, respectively. However, network-specific accelerator requires regeneration of logic and physical implementation whenever network is updated. Moreover, it certainly cannot handle cascaded network applications that are widely employed in complex real-world scenarios. Therefore, we propose a domain-specific FPGA overlay processor, named OPU to accelerate a wide range of CNN networks without re-configuration of FPGA for switch or update of CNN networks. We define our domain-specific instruction architecture with optimized granularity to maintain high efficiency while gaining extra progammability. We also built hardware micro-architectures on FPGA to verify ISA efficiency, and a compiler flow for parsing, optimization ans instructuin generation. Experiments show that OPU can achieve an average of 91% run-time MAC efficiency (RME) among various popular networks. Moreover, for VGG and YOLO networks, OPU outperforms automatically compiled network-specific accelerators in the literature. In addition, OPU shows 5.35x better power efficiency compared with Titan Xp. For a case using cascaded CNN networks, OPU is 2.9x faster compared with edge computing GPU Jetson Tx2 with a similar amount of computing resources. Our OPU platform was employed in an automatic curbside parking charging system in real-world. Using OPU as base design, we extend different versions of OPU to handle the newly emerged DCNN architectures. We have Light-OPU for light-weight DCNNs acceleration, where we modified the OPU architecture to fit the memory bounded light-weight operations. Our instruction architecture considers the sharing of major computation engine between LW operations and conventional convolution operations. This improves the run-time resource efficiency and overall power efficiency. Our experiments on seven major LW-CNNs show that Light-OPU achieves 5.5x better latency and 3.0x higher power efficiency on average compared with edge GPU NVIDIA Jetson TX2. Moreover, we also have Uni-OPU for the efficient uniform hardware acceleration of different types of transposed convolutional (TCONV) networks as well as conventional convolution (CONV) networks. Extra stage in compiler would transform the computation of Zero-inserting based TCONV (Zero-TCONV), nearest-neighbor resizing based TCONV (NN-TCONV) and CONV layers into the same pattern. The compiler conducts the following optimizations: (1) Eliminating up to 98.4% of operations in TCONV by making use of the fixed pattern of TCONV upsampling; (2) Decomposing and reformulating TCONV and CONV processes into streaming paralleled vector multiplication with uniform address generation scheme and data flow pattern. Uni-OPU can reach throughput up to 2.35 TOPS for TCONV layer. We evaluate \textit{Uni-OPU} on a benchmark set composed of six TCONV networks from different application fields. Extensive experimental results indicate that Uni-OPU is able to gain 1.45x to 3.68x superior power efficiency compared with state-of-the-art Zero-TCONV accelerators. High acceleration performance is also achieved on NN-TCONV networks, whose acceleration have not been explored before. In summary, we observe 15.04x and 12.43x higher power efficiency on Zero-TCONV and NN-TCONV networks compared with Titan Xp GPU on average. To the best of our knowledge, we are the first in-depth study to completely unify the computation process of both Zero-TCONV, NN-TCONV and CONV layers. In summary, we have been working on FPGA acceleration for deep learning vision algorithms. Several hand-coded customized accelerator as well as an auto-compiler that generates RTL code for customized accelerator have been developed. An initial tool-chain for an FPGA based overlay processor was also finished, which can compile DCNN network configuration file from popular deep learning platforms and map to processor for acceleration.

Software and Hardware Co-optimization for Deep Learning Algorithms on FPGA

Author : Chen Wu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

DOWNLOAD NOW!

Book Synopsis Software and Hardware Co-optimization for Deep Learning Algorithms on FPGA by : Chen Wu

Download or read book Software and Hardware Co-optimization for Deep Learning Algorithms on FPGA written by Chen Wu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over recent years, deep learning paradigms such as convolutional neural networks (CNNs) have shown great success in various families of tasks including object detection and au- tonomous driving, etc. To extend such success to non-euclidean data, graph convolutional networks (GCNs) have been introduced, and have quickly attracted industrial and academia attention as a popular solution to real-world problems. However, both CNNs and GCNs often have huge computation and memory complexity, which calls for specific hardware architec- tures to accelerate these algorithms. In this dissertation, we propose several architectures to accelerate CNNs and GCNs based on FPGA platforms. We start from the domain-specific FPGA-overlay processor (OPU) on commonly used CNNs, such as VGG, Inception, ResNet, and YoloV2. The data is first quantized to 8-bit fixed-point with little accuracy loss to reduce computation complexity and memory require- ment. A fully-pipelined dataflow architecture is proposed to accelerate the typical layers (i.e., convolutional, pooling, residual, inception, and activation layers) in CNNs. Experi- mental results show that OPU is 9.6 faster than GPU Jetson TX2 on a cascaded of three CNNs, which are used for the curbside parking system. However, 8-bit fixed-point data representation always need re-training to maintain accu- racy for deep CNNs. In this way, we propose a low precision (8-bit) floating-point (LPFP) quantization method for FPGA-based acceleration to overcome the above limitation. With- out any re-training, LPFP finds an optimal 8-bit data representation with negligible top- 1/top-5 accuracy loss (within 0.5%/0.3% in our experiments, respectively, and significantly better than existing methods for deep CNNs). Furthermore, we implement one 8-bit LPFP multiplication by one 4-bit multiply-adder (MAC) and one 3-bit adder. Therefore, we can implement four 8-bit LPFP multiplications using one DSP48E1 of Xilinx Kintex-7 family or one DSP48E2 of Xilinx Ultrascale/Ultrascale Plus family whereas one DSP can only imple- ment two 8-bit fixed-point multiplications. Experiments on six typical CNNs for inference show that on average, we improve throughput by 1.5 over existing FPGA accelerators. Particularly for VGG16 and Yolo, compared with seven FPGA accelerators, we improve average throughput by 3.5 and 27.5 and average throughput per DSP by 4.1 and 5, respectively. CNNs quantized with mixed precision, on the other hand, benefits from low precision while maintaining accuracy. To better leverage the advantages of mixed precision, we propose a Mixed Precision FPGA-based Overlay Processor (MP-OPU) for both conventional and lightweight CNNs. The micro-architecture of MP-OPU considers sharing of computation core with mixed precision weights and activations to improve computation efficiency. In addition, run-time scheduling of external memory access and data arrangement are optimized to further leverage the advantages of mixed precision data representation. Our experimental results show that MP-OPU reaches 4.92 TOPS peak throughput when implemented on Xilinx VC709 FPGA (with all DSPs configured to support 2-bit multipliers). Moreover, MP-OPU achieves 12.9 latency reduction and 2.2 better throughput per DSP for conventional CNNs, while 7.6 latency reduction and 2.9 better throughput per DSP for lightweight CNNs, all on average compared with existing FPGA accelerators/processors, respectively. Graph convolutional networks (GCNs) have been introduced to effectively process non-euclidean graph data. However, GCNs incur large amount of irregularity in computation and memory access, which prevents efficient use of previous CNN accelerators/processors. In this way, we propose a lightweight FPGA-based accelerator, named LW-GCN, to tackle irregularity in computation and memory access in GCN inference. We first decompose the main GCN operations into Sparse Matrix-Matrix Multiplication (SpMM) and Matrix-Matrix Multiplication (MM). Thereafter, we propose a novel compression format to balance work- load across PEs and prevent data hazards. In addition, we quantize the data into 16-bit fixed-point and apply workload tiling, and map both SpMM and MM onto a uniform archi- tecture on resource limited devices. Evaluations on GCN and GraphSAGE are performed on Xilinx Kintex-7 FPGA with three popular datasets. Compared with existing CPU, GPU and state-of-the-art FPGA-based accelerator, LW-GCN reduces latency by up to 60, 12 and 1.7 and increases power efficiency by up to 912, 511 and 3.87, respectively. Moreover, compared with Nvidia's latest edge GPU Jetson Xavier NX, LW-GCN achieves speedup and energy savings of 32 and 84, respectively. At last, we extend our GCN inference accelerator to a GCN training accelerator, called SkeletonGCN. To better fit the properties of GCN training, we add more software-hardware co-optimizations. First, we simplify the non-linear operations in GCN training to better fit the FPGA computation, and identify reusable intermediate results to eliminate redundant computation. Second, we optimize the previous compression format to further reduce mem- ory bandwidth while allowing efficient decompression on hardware. Finally, we propose a unified architecture to support SpMM, MM and MM with transpose, all on the same group of PEs to increase DSP utilization on FPGA. Evaluations are performed on Xilinx Alveo U200 board. Compared with existing FPGA-based accelerator on the same network archi- tecture, SkeletonGCN can achieve up to 11.3 speedup while maintaining the same training accuracy with 16-bit fixed-point data representation. In addition, SkeletonGCN is 178 and 13.1 faster than state-of-the-art CPU and GPU implementation on popular datasets, respectively. To summarize, we have been working on FPGA-based acceleration for deep learning algorithms of CNNs and GCNs in both inference and training process. All the accelera- tors/processors were hand-coded and have been fully verified. In addition, the related tool chains for generating golden results and running instructions for the accelerators/processors have also been finished.

FPGA Implementations of Neural Networks

Author : Amos R. Omondi
Publisher : Springer Science & Business Media
ISBN 13 : 0387284877
Total Pages : 365 pages
Book Rating : 4.3/5 (872 download)

DOWNLOAD NOW!

Book Synopsis FPGA Implementations of Neural Networks by : Amos R. Omondi

Download or read book FPGA Implementations of Neural Networks written by Amos R. Omondi and published by Springer Science & Business Media. This book was released on 2006-10-04 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

Application of FPGA to Real‐Time Machine Learning

Author : Piotr Antonik
Publisher : Springer
ISBN 13 : 3319910531
Total Pages : 187 pages
Book Rating : 4.3/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Application of FPGA to Real‐Time Machine Learning by : Piotr Antonik

Download or read book Application of FPGA to Real‐Time Machine Learning written by Piotr Antonik and published by Springer. This book was released on 2018-05-18 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Techniques for Mapping Deep Neural Network Frameworks to Programmable Accelerators

Author : Stefan Hadjis
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.5/5 (442 download)

DOWNLOAD NOW!

Book Synopsis Techniques for Mapping Deep Neural Network Frameworks to Programmable Accelerators by : Stefan Hadjis

Download or read book Techniques for Mapping Deep Neural Network Frameworks to Programmable Accelerators written by Stefan Hadjis and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The trend towards increasing specialization in DNN accelerators is first discussed, as well as why FPGA hardware is sometimes selected. The two major ways that DNN applications can be automatically mapped to FPGAs are then reviewed: (1) mapping to manually-optimized template designs or overlay architectures, which is suited to DNN frameworks as a mapping source, and (2) mapping by compiling automatically-designed hardware. Next, an open-source, end-to-end toolchain to map TensorFlow DNNs to cloud FPGAs is described, which is the first open-source toolchain to use a modern DNN framework as a starting point and either (1) target public cloud FPGA hardware or (2) compile DNNs reaching state-of-the-art accuracy on an FPGA (cloud or not). This compiler is used to explore tradeoffs in DNN to FPGA mapping, including tensor storage format and architecture specialization, and to examine how different layer dimensions and other characteristics, such as locality, affect design decisions. Next, optimizations to improve circuits automatically designed by hardware compilation tools and DSLs are investigated. An algorithm for high-level hardware compilers is presented which reduces resource utilization for on-chip memory accesses common in DNNs and computer vision. Its applicability to general dense access patterns and applications is also demonstrated. For each of these observations, generalization is made beyond DNN or ML domains, and examples are shown where increasing specialization or heterogeneity in storage formats, processor architecture and on-chip data structures can improve FPGA accelerator resource utilization, timing closure and bandwidth requirements.

Proceedings of Third International Conference on Sustainable Expert Systems

Author : Subarna Shakya
Publisher : Springer Nature
ISBN 13 : 9811978743
Total Pages : 1025 pages
Book Rating : 4.8/5 (119 download)

DOWNLOAD NOW!

Book Synopsis Proceedings of Third International Conference on Sustainable Expert Systems by : Subarna Shakya

Download or read book Proceedings of Third International Conference on Sustainable Expert Systems written by Subarna Shakya and published by Springer Nature. This book was released on 2023-02-22 with total page 1025 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features high-quality research papers presented at the 3rd International Conference on Sustainable Expert Systems (ICSES 2022), held in Nepal during September 9–10, 2022. The book focuses on the research information related to artificial intelligence, sustainability and expert systems applied in almost all the areas of industries, government sectors and educational institutions worldwide. The main thrust of the book is to publish the conference papers that deal with the design, implementation, development, testing and management of intelligent and sustainable expert systems and also to provide both theoretical and practical guidelines for the deployment of these systems.

Towards Ubiquitous Low-power Image Processing Platforms

Author : Magnus Jahre
Publisher : Springer Nature
ISBN 13 : 3030535320
Total Pages : 264 pages
Book Rating : 4.0/5 (35 download)

DOWNLOAD NOW!

Book Synopsis Towards Ubiquitous Low-power Image Processing Platforms by : Magnus Jahre

Download or read book Towards Ubiquitous Low-power Image Processing Platforms written by Magnus Jahre and published by Springer Nature. This book was released on 2020-12-15 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the key scientific outcomes of the Horizon 2020 research project TULIPP: Towards Ubiquitous Low-power Image Processing Platforms. The main focus lies on the development of high-performance, energy-efficient embedded systems for the growing range of increasingly complex image processing applications. The holistic TULIPP approach is described in the book, which addresses hardware platforms, programming tools and embedded operating systems. Several of the results are available as open-source hardware/software for the community. The results are evaluated with several use cases taken from real-world applications in key domains such as Unmanned Aerial Vehicles (UAVs), robotics, space and medicine. Discusses the development of high-performance, energy-efficient embedded systems for the growing range of increasingly complex image processing applications; Covers the hardware architecture of embedded image processing systems, novel methods, tools and libraries for programming those systems as well as embedded operating systems to manage those systems; Demonstrates results with several challenging applications, such as medical systems, robotics, drones and automotive.

Computer Vision – ECCV 2020 Workshops

Author : Adrien Bartoli
Publisher : Springer Nature
ISBN 13 : 3030682382
Total Pages : 752 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!

Book Synopsis Computer Vision – ECCV 2020 Workshops by : Adrien Bartoli

Download or read book Computer Vision – ECCV 2020 Workshops written by Adrien Bartoli and published by Springer Nature. This book was released on 2021-01-30 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 6-volume set, comprising the LNCS books 12535 until 12540, constitutes the refereed proceedings of 28 out of the 45 workshops held at the 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic. The 249 full papers, 18 short papers, and 21 further contributions included in the workshop proceedings were carefully reviewed and selected from a total of 467 submissions. The papers deal with diverse computer vision topics. Part V includes: The 16th Embedded Vision Workshop; Real-World Computer Vision from Inputs with Limited Quality (RLQ); The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS 2020); The Visual Object Tracking Challenge Workshop (VOT 2020); and Video Turing Test: Toward Human-Level Video Story Understanding.

Hardware Accelerators in Data Centers

Author : Christoforos Kachris
Publisher : Springer
ISBN 13 : 3319927922
Total Pages : 280 pages
Book Rating : 4.3/5 (199 download)

DOWNLOAD NOW!

Book Synopsis Hardware Accelerators in Data Centers by : Christoforos Kachris

Download or read book Hardware Accelerators in Data Centers written by Christoforos Kachris and published by Springer. This book was released on 2018-08-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.

Applied Reconfigurable Computing. Architectures, Tools, and Applications

Author : Steven Derrien
Publisher : Springer Nature
ISBN 13 : 3030790258
Total Pages : 338 pages
Book Rating : 4.0/5 (37 download)

DOWNLOAD NOW!

Book Synopsis Applied Reconfigurable Computing. Architectures, Tools, and Applications by : Steven Derrien

Download or read book Applied Reconfigurable Computing. Architectures, Tools, and Applications written by Steven Derrien and published by Springer Nature. This book was released on 2021-06-23 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 17th International Symposium on Applied Reconfigurable Computing, ARC 2021, held as a virtual event, in June 2021. The 14 full papers and 11 short presentations presented in this volume were carefully reviewed and selected from 40 submissions. The papers cover a broad spectrum of applications of reconfigurable computing, from driving assistance, data and graph processing acceleration, computer security to the societal relevant topic of supporting early diagnosis of Covid infectious conditions.

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Author : Muhammad Mohid Nabil
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (19 download)

DOWNLOAD NOW!

Book Synopsis FPGA Implementation of Reduced Precision Convolutional Neural Networks by : Muhammad Mohid Nabil

Download or read book FPGA Implementation of Reduced Precision Convolutional Neural Networks written by Muhammad Mohid Nabil and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the improvement in processing systems, machine learning applications are finding widespread use in almost all sectors of technology. Image recognition is one application of machine learning which has become widely popular with various architectures and systems aimed at improving recognition performance. With classification accuracy now approaching saturation point, many researchers are now focusing on resource and energy efficiency. With the increased demand for learning applications in embedded devices, it is of paramount importance to optimize power and energy consumption to increase utility in these low power embedded systems. In recent months, reduced precision neural networks have caught the attention of some researchers. Reduced data width deep nets offer the potential of saving valuable resources on hardware platforms. In turn, these hardware platforms such as Field Programmable Gate Arrays (FPGAs) offer the potential of a low power system with massive parallelism increasing throughput and performance. In this research, we explore the implementations of a deep learning architecture on FPGA in the presence of resource and energy constraints. We study reduced precision neural networks and implement one such architecture as a proof of concept. We focus on binarized convolutional neural network and its implementation on FPGAs. Binarized convolutional nets have displayed a classification accuracy of up to 88% with some smaller image sets such as CIFAR-10. This number is on the rise with some of the new architectures. We study the tradeoff between architecture depth and its impact on accuracy to get a better understanding of the convolutional layers and their impact on the overall performance. This is done from a hardware perspective giving us better insight enabling better resource allocation on FPGA fabric. Zynq ZCU-102 has been used for accelerator implementation. High level synthesis tool (Vivado HLS) from Xilinx is used for CNN definition on FPGA fabric.

Introduction to LabVIEW FPGA for RF, Radar, and Electronic Warfare Applications

Author : Terry Stratoudakis
Publisher : Artech House
ISBN 13 : 1630817945
Total Pages : 270 pages
Book Rating : 4.6/5 (38 download)

DOWNLOAD NOW!

Book Synopsis Introduction to LabVIEW FPGA for RF, Radar, and Electronic Warfare Applications by : Terry Stratoudakis

Download or read book Introduction to LabVIEW FPGA for RF, Radar, and Electronic Warfare Applications written by Terry Stratoudakis and published by Artech House. This book was released on 2021-01-31 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-time testing and simulation of open- and closed-loop radio frequency (RF) systems for signal generation, signal analysis and digital signal processing require deterministic, low-latency, high-throughput capabilities afforded by user reconfigurable field programmable gate arrays (FPGAs). This comprehensive book introduces LabVIEW FPGA, provides best practices for multi-FPGA solutions, and guidance for developing high-throughput, low-latency FPGA based RF systems. Written by a recognized expert with a wealth of real-world experience in the field, this is the first book written on the subject of FPGAs for radar and other RF applications.

An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs

Author : Sachin Kumawat
Publisher :
ISBN 13 : 9780355764413
Total Pages : pages
Book Rating : 4.7/5 (644 download)

DOWNLOAD NOW!

Book Synopsis An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs by : Sachin Kumawat

Download or read book An OpenCL Framework for Real-time Inference of Next-generation Convolutional Neural Networks on FPGAs written by Sachin Kumawat and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Convolutional Neural Networks (CNNs) consist of billions of multiplications and additions which require the use of parallel computing units such as GPUs, FPGAs and other DSP processors. Consequently, General Purpose GPU (GPGPU) computing has taken this field by storm. At the same time, there has been an increased interest in FPGA based acceleration of CNN inference. In this work, we present FICaffe, a framework for FPGA-based Inference with Caffe, which provides a complete automated generation and mapping of CNN accelerators on FPGAs. We target applications with critical latency requirements and design high processing efficiency accelerators for CNNs. The architecture is structured in a highly concurrent OpenCL library, which enables High Level Synthesis tools to effectively exploit data, task and pipeline parallelism. We propose a unified memory model, that drives exploration of optimal design by matching on-chip and off-chip memory bandwidths available on FPGA platforms. We also identify origins of all clock cycle stalls and overheads inherent to CNN acceleration designs and provide a detailed model to accurately predict the runtime latency with less than 4% error against on-board tests. Furthermore, with FICaffe we provide support for cross-network synthesis, such that it is possible to processes a variety of CNNs, with reasonable efficiency, without long re-compilation hours. FICaffe is integrated with the popular deep learning framework Caffe, and is deployable to a wide variety of CNNs. FICaffe's efficacy is shown by mapping to a 28nm Stratix V GXA7 chip, and both network specific and cross-network performance are reported for AlexNet, VGG, SqueezeNet and GoogLeNet. We show a processing efficiency of 95.8% for the widely-reported VGG benchmark, which outperforms prior work. FICaffe also achieves more than 2X speedup on Stratix V GXA7 compared with the best published results on this chip, to the best of our knowledge.

FPGA Based Multi-core Architectures for Deep Learning Networks

Author : Hua Chen
Publisher :
ISBN 13 :
Total Pages : 42 pages
Book Rating : 4.:/5 (939 download)

DOWNLOAD NOW!

Book Synopsis FPGA Based Multi-core Architectures for Deep Learning Networks by : Hua Chen

Download or read book FPGA Based Multi-core Architectures for Deep Learning Networks written by Hua Chen and published by . This book was released on 2015 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning a large scalable network architecture based on neural network. It is currently an extremely active research area in machine learning and pattern recognition society. They have diverse uses including pattern recognition, signal processing, image processing, image compression, classification of remote sensing data, and big data processing. Interest in specialized architectures for accelerating deep learning networks has increased significantly because of their ability to reduce power, increase performance, and allow fault tolerant computing. Specialized neuromorphic architectures could provide high performance at extreme low powers for these applications. This thesis concentrates on the implementation of multi-core neuromorphic network architecture on FPGA. Hardware prototyping of wormhole router unit is developed to control transmission of data packets running through between cores. Router units connect multiple cores into a large scalable network. This network is programmed on a Stratix IV FPGA board. Additionally, a memory initialization system is design inside the core to realize external network configuration. In this approaching, different applications could be mapped on the network without repeating FPGA compilation. One application called Image Edge Detection is mapped on the network. Finally this network outputs the desired image and demonstrate 3.4x run time efficiency and 3.6x energy-delay efficiency by FPGA implementation.

Architecture of Computing Systems

Author : Dietmar Fey
Publisher : Springer Nature
ISBN 13 : 303166146X
Total Pages : 368 pages
Book Rating : 4.0/5 (316 download)

DOWNLOAD NOW!

Book Synopsis Architecture of Computing Systems by : Dietmar Fey

Download or read book Architecture of Computing Systems written by Dietmar Fey and published by Springer Nature. This book was released on with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt:

TinyML

Author : Pete Warden
Publisher : O'Reilly Media
ISBN 13 : 1492052019
Total Pages : 504 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis TinyML by : Pete Warden

Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

A Soft Processor Overlay with Tightly-Coupled FPGA Accelerator

Author : Ho-Cheung Ng
Publisher :
ISBN 13 : 9781361035634
Total Pages : pages
Book Rating : 4.0/5 (356 download)

DOWNLOAD NOW!

Book Synopsis A Soft Processor Overlay with Tightly-Coupled FPGA Accelerator by : Ho-Cheung Ng

Download or read book A Soft Processor Overlay with Tightly-Coupled FPGA Accelerator written by Ho-Cheung Ng and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "A Soft Processor Overlay With Tightly-coupled FPGA Accelerator" by Ho-cheung, Ng, 吳浩彰, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: FPGA overlays have shown the potential to improve designers' productivity through balancing flexibility and ease of configuration of the underlying fabric while maintaining considerable overall performance promised by FPGAs. To truly facilitate full application acceleration, it is often necessary to also include a highly efficient processor that integrates and collaborates with the accelerators while maintaining the benefits of being implemented within the same overlay framework. This thesis presents an open-source soft processor that is tightly-coupled with FPGA accelerator as part of an overlay framework. RISC-V is chosen as the instruction set for its openness and simplicity, and the soft processor is designed as a 4-stage pipeline to balance resource consumption and performance when implemented on FPGAs. The processor is generically implemented so as to promote design portability and compatibility across different FPGA platforms. Experiment shows that the integrated software-hardware applications using the proposed tightly-coupled architecture achieve comparable performance as hardware-only accelerators while the proposed architecture provides additional run-time flexibility. The processor can be synthesized to both low-end and high-performance FPGA families from different vendors, achieving the highest frequency of 268:67MHz on Virtex-7 device. Synthesized results of the soft processor also display improvement on FPGA resource consumption and efficiency when compared to existing RISC-V design. In addition, this thesis also presents an FPGA-centric approach that allows gateware to directly access the virtual memory space as part of the executing process without involving the CPU. It allows efficient access to memory in heterogeneous systems and complements traditional software-centric approach by providing a simplified memory access model to improve designers' productivity and high-level compilation tools portability. In this approach, a caching address translation buffer was implemented alongside the user FPGA gateware to provide runtime mapping between virtual and physical memory addresses. It coordinates with the OS running on the CPU to update address translations and to maintain memory consistency. The system was implemented on a commercial off-the-shelf FPGA add-on card to demonstrate the viability of such approach in low-cost systems. Experiment with a 2D stencil computing application implemented with this FPGA-centric approach results in reasonable performance improvement when compared to a typical software-centric implementation; while the number of context switches between FPGA and CPU in both kernel and user mode was significantly reduced, freeing the CPU for other concurrent user tasks. Subjects: Field programmable gate arrays