Fpga Overlay Processor For Deep Neural Networks

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FPGA Overlay Processor for Deep Neural Networks

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

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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)

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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.

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)

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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.

FPGA Implementations of Neural Networks

Author : Amos R. Omondi
Publisher : Springer Science & Business Media
ISBN 13 : 9780387284859
Total Pages : 380 pages
Book Rating : 4.2/5 (848 download)

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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 2006-04-21 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of neural networks has now reached the stage where they are employed in a large variety of practical contexts. However, to date the majority of such implementations have been in software. While it is generally recognised that hardware implementations could, through performance advantages, greatly increase the use of neural networks, to date the relatively high cost of developing Application-Specific Integrated Circuits (ASICs) has meant that only a small number of hardware neurocomputers has gone beyond the research-prototype stage. The situation has now changed dramatically: with the appearance of large, dense, highly parallel FPGA circuits it has now become possible to envisage putting large-scale neural networks in hardware, to get high performance at low costs. This in turn makes it practical to develop hardware neural-computing devices for a wide range of applications, ranging from embedded devices in high-volume/low-cost consumer electronics to large-scale stand-alone neurocomputers. Not surprisingly, therefore, research in the area has recently rapidly increased, and even sharper growth can be expected in the next decade or so. Nevertheless, the many opportunities offered by FPGAs also come with many challenges, since most of the existing body of knowledge is based on ASICs (which are not as constrained as FPGAs). These challenges range from the choice of data representation, to the implementation of specialized functions, through to the realization of massively parallel neural networks; and accompanying these are important secondary issues, such as development tools and technology transfer. All these issues are currently being investigated by a large number of researchers, who start from different bases and proceed by different methods, in such a way that there is no systematic core knowledge to start from, evaluate alternatives, validate claims, and so forth. FPGA Implementations of Neural Networks aims to be a timely one that fill this gap in three ways: First, it will contain appropriate foundational material and therefore be appropriate for advanced students or researchers new to the field. Second, it will capture the state of the art, in both depth and breadth and therefore be useful researchers currently active in the field. Third, it will cover directions for future research, i.e. embryonic areas as well as more speculative ones.

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)

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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.

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)

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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.

Robotic Computing on FPGAs

Author : Shaoshan Liu
Publisher : Springer Nature
ISBN 13 : 3031017714
Total Pages : 202 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Robotic Computing on FPGAs by : Shaoshan Liu

Download or read book Robotic Computing on FPGAs written by Shaoshan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the state-of-the-art field-programmable gate array (FPGA)-based robotic computing accelerator designs and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots.

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)

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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

FPGA Implementation of Reduced Precision Convolutional Neural Networks

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

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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.

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)

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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)

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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.

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

Author : Xinyu Zhang
Publisher :
ISBN 13 :
Total Pages : 56 pages
Book Rating : 4.:/5 (981 download)

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Book Synopsis A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA by : Xinyu Zhang

Download or read book A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA written by Xinyu Zhang and published by . This book was released on 2017 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors. However, to date, there has been little research on the use of FPGA implementations of deconvolutional neural networks (DCNNs). DCNNs, also known as generative CNNs, encode high-dimensional probability distributions and have been widely used for computer vision applications such as scene completion, scene segmentation, image creation, image denoising, and super-resolution imaging. We propose an FPGA architecture for deconvolutional networks built around an accelerator which effectively handles the complex memory access patterns needed to perform strided deconvolutions, and that supports convolution as well. We also develop a three-step design optimization method that systematically exploits statistical analysis, design space exploration and VLSI optimization. To verify our FPGA deconvolutional accelerator design methodology we train DCNNs offline on two representative datasets using the generative adversarial network method (GAN) run on Tensorflow, and then map these DCNNs to an FPGA DCNN-plus-accelerator implementation to perform generative inference on a Xilinx Zynq-7000 FPGA. Our DCNN implementation achieves a peak performance density of 0.012 GOPs/DSP.

A Dual-engine Fetch/Compute Overlay Processor for FPGAs

Author : Rafat Rashid
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis A Dual-engine Fetch/Compute Overlay Processor for FPGAs by : Rafat Rashid

Download or read book A Dual-engine Fetch/Compute Overlay Processor for FPGAs written by Rafat Rashid and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform

Author : Md Syadus Sefat
Publisher :
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform by : Md Syadus Sefat

Download or read book Design and Performance Analysis of Hardware Accelerator for Deep Neural Network in Heterogeneous Platform written by Md Syadus Sefat and published by . This book was released on 2018 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a new flexible approach to implementing energy-efficient DNN accelerator on FPGAs. Our design leverages the Coherent Accelerator Processor Interface (CAPI) which provides a cache-coherent view of system memory to attached accelerators. Computational kernels are accelerated on a CAPI-supported Kintex FPGA board. Our implementation bypasses the need for device driver code and significantly reduces the communication and I/O transfer overhead. To improve the performance of the entire application, we propose a collaborative model of execution in which the control of the data flow within the accelerator is kept independent, freeing-up CPU cores to work on other parts of the application. For further performance enhancements, we propose a technique to exploit data locality in the cache, situated in the CAPI Power Service Layer (PSL). Finally, we develop a resource-conscious implementation for more efficient utilization of resources and improved scalability. Compared with the previous work, our architecture achieves both improved performance and better power efficiency.

FPGA Logic Block Architectures for Efficient Deep Learning Inference

Author : Mohamed Eldafrawy
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis FPGA Logic Block Architectures for Efficient Deep Learning Inference by : Mohamed Eldafrawy

Download or read book FPGA Logic Block Architectures for Efficient Deep Learning Inference written by Mohamed Eldafrawy and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reducing the precision of deep neural networks can yield large efficiency gains with little or no accuracy degradation compared to single-precision floating-point representation. A wide range of precisions fall on the pareto-optimal curve of hardware efficiency vs. accuracy with no single precision dominating, making the variable precision capabilities of field-programmable gate arrays (FPGAs) very valuable. This thesis proposes six FPGA logic block architectures that improve the area efficiency of multiplications and additions implemented in the soft fabric. Increasing the look-up table fracturability and adding two adders to the adaptive logic module leads to a 1.5x area reduction for machine learning (ML) kernels and increases their speed, while simultaneously reducing the area of general applications by 6%. On the other hand, adding a 9-bit shadow multiplier to logic blocks reduces ML kernels' area by 2.4x and critical path delay by 1.4x, but increases the area of general applications by 15%.

Reconfigurable Hardware Acceleration of CNNs on FPGA-based Smart Cameras

Author : Kamel Abdelouahab
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (18 download)

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Book Synopsis Reconfigurable Hardware Acceleration of CNNs on FPGA-based Smart Cameras by : Kamel Abdelouahab

Download or read book Reconfigurable Hardware Acceleration of CNNs on FPGA-based Smart Cameras written by Kamel Abdelouahab and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Convolutional Neural Networks (CNNs) have become a de-facto standard in computer vision. This success came at the price of a high computational cost, making the implementation of CNNs, under real-time constraints, a challenging task.To address this challenge, the literature exploits the large amount of parallelism exhibited by these algorithms, motivating the use of dedicated hardware platforms. In power-constrained environments, such as smart camera nodes, FPGA-based processing cores are known to be adequate solutions in accelerating computer vision applications. This is especially true for CNN workloads, which have a streaming nature that suits well to reconfigurable hardware architectures.In this context, the following thesis addresses the problems of CNN mapping on FPGAs. In Particular, it aims at improving the efficiency of CNN implementations through two main optimization strategies; The first one focuses on the CNN model and parameters while the second one considers the hardware architecture and the fine-grain building blocks.

Reconfigurable Convolution Implementation for CNNs in FPGAs

Author : Jesse Bannon
Publisher :
ISBN 13 :
Total Pages : 18 pages
Book Rating : 4.:/5 (19 download)

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Book Synopsis Reconfigurable Convolution Implementation for CNNs in FPGAs by : Jesse Bannon

Download or read book Reconfigurable Convolution Implementation for CNNs in FPGAs written by Jesse Bannon and published by . This book was released on 2018 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning continues to be the revolutionary method used in pattern recognition applications including image, video, and speech processing. Convolutional Neural Networks (CNNs) in particular have outperformed every competitor in image classification benchmarks, but suffer from high computation and storage complexities. It is becoming more apparent to extend this breakthrough technology to embedded applications that demand low power and mission critical response times. Consequently, embedded CNNs deployed on the edge require compact platforms capable of accelerated computing. Previous works have explored methods to optimize convolution computation within Field Programmable Gate Arrays (FPGAs). Many of which only consider supporting a single CNN architecture. While this approach allows precise optimizations structured around a specific CNN, it restricts the FPGA from updating its model without tremendous compile times upwards to hours. In this work, we explore state-of-the-art CNN-FPGA architectures and implement our own reconfigurable convolution computation unit (CCU) using the Intel High Level Synthesis (HLS) Compiler using a sliding window-based implementation. Results show our CCU does not suffice for real-time computations on the edge.