An Fpga Based Hardware Accelerator For The Digital Image Correlation Engine PDF Download
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| Author | : Keaten Stokke |
| Publisher | : |
| Total Pages | : 278 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Download An FPGA-based Hardware Accelerator for the Digital Image Correlation Engine Book in PDF, ePub and Kindle
The work presented in this thesis was aimed at the development of a hardware accelerator for the Digital Image Correlation engine (DICe) and compare two methods of data access, USB and Ethernet. The original DICe software package was created by Sandia National Laboratories and is written in C++. The software runs on any typical workstation PC and performs image correlation on available frame data produced by a camera. When DICe is introduced to a high volume of frames, the correlation time is on the order of days. The time to process and analyze data with DICe becomes a concern when a high-speed camera, like the Phantom VEO 1310, is used which is capable of recording up to 10,000 Frames Per Second (FPS) [1]. To reduce this correlation time the DICe software package was ported over to Verilog, and a Xilinx UltraScale+ MPSoC ZCU104 FPGA was targeted for the design. FPGAs are used to implement the hardware accelerator due to their hardware-level speeds and reprogrammability. The ZCU104 board contains FPGA fabric on the Programmable Logic (PL) side that is used for the implementation of the ported DICe hardware design. On the Processing System (PS) side of the ZCU104, a quad-core ARM Cortex-A53 processor is available that runs the Ubuntu 18.04 LTS Linux-based kernel to provide the drivers for USB and Ethernet I/O, a standard file system that is accessed through a Command-Line Interface (CLI), and to run the program's control scripts that are written in C. This work compares the processing time of the DICe hardware accelerator when frame data is accessed via Ethernet-stream or local USB to showcase the fastest option when using DICe. Both methods of accessing frame data are necessary because data may be offloaded from the camera over Ethernet while it is still recording, or the frame data may be readily available in memory. By providing both a method to access frame data via USB and Ethernet, users have more flexibility when using the DICe hardware accelerator. The work presented in this thesis is significant because it is the first known hardware accelerator for the DICe software.
| Author | : Donald G. Bailey |
| Publisher | : John Wiley & Sons |
| Total Pages | : 501 |
| Release | : 2023-08-08 |
| Genre | : Technology & Engineering |
| ISBN | : 1119819814 |
Download Design for Embedded Image Processing on FPGAs Book in PDF, ePub and Kindle
Design for Embedded Image Processing on FPGAs Bridge the gap between software and hardware with this foundational design reference Field-programmable gate arrays (FPGAs) are integrated circuits designed so that configuration can take place. Circuits of this kind play an integral role in processing images, with FPGAs increasingly embedded in digital cameras and other devices that produce visual data outputs for subsequent realization and compression. These uses of FPGAs require specific design processes designed to mediate smoothly between hardware and processing algorithm. Design for Embedded Image Processing on FPGAs provides a comprehensive overview of these processes and their applications in embedded image processing. Beginning with an overview of image processing and its core principles, this book discusses specific design and computation techniques, with a smooth progression from the foundations of the field to its advanced principles. Readers of the second edition of Design for Embedded Image Processing on FPGAs will also find: Detailed discussion of image processing techniques including point operations, histogram operations, linear transformations, and more New chapters covering Deep Learning algorithms and Image and Video Coding Example applications throughout to ground principles and demonstrate techniques Design for Embedded Image Processing on FPGAs is ideal for engineers and academics working in the field of Image Processing, as well as graduate students studying Embedded Systems Engineering, Image Processing, Digital Design, and related fields.
| Author | : Iouliia Skliarova |
| Publisher | : Springer |
| Total Pages | : 245 |
| Release | : 2019-05-30 |
| Genre | : Technology & Engineering |
| ISBN | : 3030207218 |
Download FPGA-BASED Hardware Accelerators Book in PDF, ePub and Kindle
This book suggests and describes a number of fast parallel circuits for data/vector processing using FPGA-based hardware accelerators. Three primary areas are covered: searching, sorting, and counting in combinational and iterative networks. These include the application of traditional structures that rely on comparators/swappers as well as alternative networks with a variety of core elements such as adders, logical gates, and look-up tables. The iterative technique discussed in the book enables the sequential reuse of relatively large combinational blocks that execute many parallel operations with small propagation delays. For each type of network discussed, the main focus is on the step-by-step development of the architectures proposed from initial concepts to synthesizable hardware description language specifications. Each type of network is taken through several stages, including modeling the desired functionality in software, the retrieval and automatic conversion of key functions, leading to specifications for optimized hardware modules. The resulting specifications are then synthesized, implemented, and tested in FPGAs using commercial design environments and prototyping boards. The methods proposed can be used in a range of data processing applications, including traditional sorting, the extraction of maximum and minimum subsets from large data sets, communication-time data processing, finding frequently occurring items in a set, and Hamming weight/distance counters/comparators. The book is intended to be a valuable support material for university and industrial engineering courses that involve FPGA-based circuit and system design.
| Author | : Dhaval N. Vyas |
| Publisher | : |
| Total Pages | : 236 |
| Release | : 2005 |
| Genre | : Computer algorithms |
| ISBN | : |
Download FPGA-based Hardware Accelerator Design for Performance Improvement of a System-on-a-chip Application Book in PDF, ePub and Kindle
| Author | : Wei Wang |
| Publisher | : |
| Total Pages | : |
| Release | : 2013 |
| Genre | : University of Ottawa theses |
| ISBN | : |
Download Accessing an FPGA-based Hardware Accelerator in a Paravirtualized Environment Book in PDF, ePub and Kindle
In this thesis we present pvFPGA, the first system design solution for virtualizing an FPGA - based hardware accelerator on the x86 platform. The accelerator design on the FPGA can be used for accelerating various applications, regardless of the application computation latencies. Our design adopts the Xen virtual machine monitor (VMM) to build a paravirtualized environment, and a Xilinx Virtex - 6 as an FPGA accelerator. The accelerator communicates with the x86 server via PCI Express (PCIe). In comparison to the current GPU virtualization solutions, which primarily intercept and redirect API calls to the hosted or privileged domain's user space, pvFPGA virtualizes an FPGA accelerator directly at the lower device driver layer. This gives rise to higher efficiency and lower overhead. In pvFPGA, each unprivileged domain allocates a shared data pool for both user - kernel and inter-domain data transfer. In addition, we propose the coprovisor, a new component that enables multiple domains to simultaneously access an FPGA accelerator. The experimental results have shown that 1) pvFPGA achieves close-to-zero overhead compared to accessing the FPGA accelerator without the VMM layer, 2) the FPGA accelerator is successfully shared by multiple domains, 3) distributing different maximum data transfer bandwidths to different domains can be achieved by regulating the size of the shared data pool at the split driver loading time, 4) request turnaround time is improved through DMA (Direct Memory Access) context switches implemented by the coprovisor.
| Author | : Shaoshan Liu |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 220 |
| Release | : 2021-06-30 |
| Genre | : Computers |
| ISBN | : 1636391664 |
Download Robotic Computing on FPGAs Book in PDF, ePub and Kindle
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.
| Author | : Hamed Farhadi |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 231 |
| Release | : 2018-09-19 |
| Genre | : Computers |
| ISBN | : 1789237521 |
Download Machine Learning Book in PDF, ePub and Kindle
The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.
| Author | : Albert Chun-Chen Liu |
| Publisher | : John Wiley & Sons |
| Total Pages | : 244 |
| Release | : 2021-08-23 |
| Genre | : Computers |
| ISBN | : 1119810477 |
Download Artificial Intelligence Hardware Design Book in PDF, ePub and Kindle
ARTIFICIAL INTELLIGENCE HARDWARE DESIGN Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field In Artificial Intelligence Hardware Design: Challenges and Solutions, distinguished researchers and authors Drs. Albert Chun Chen Liu and Oscar Ming Kin Law deliver a rigorous and practical treatment of the design applications of specific circuits and systems for accelerating neural network processing. Beginning with a discussion and explanation of neural networks and their developmental history, the book goes on to describe parallel architectures, streaming graphs for massive parallel computation, and convolution optimization. The authors offer readers an illustration of in-memory computation through Georgia Tech’s Neurocube and Stanford’s Tetris accelerator using the Hybrid Memory Cube, as well as near-memory architecture through the embedded eDRAM of the Institute of Computing Technology, the Chinese Academy of Science, and other institutions. Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition Perfect for hardware and software engineers and firmware developers, Artificial Intelligence Hardware Design is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.
| Author | : Vivienne Sze |
| Publisher | : Springer Nature |
| Total Pages | : 254 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017668 |
Download Efficient Processing of Deep Neural Networks Book in PDF, ePub and Kindle
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
| Author | : Shiho Kim |
| Publisher | : Elsevier |
| Total Pages | : 414 |
| Release | : 2021-04-07 |
| Genre | : Computers |
| ISBN | : 0128231238 |
Download Hardware Accelerator Systems for Artificial Intelligence and Machine Learning Book in PDF, ePub and Kindle
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
