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Author : Anonim
Publisher : Unknown
Page : 0 pages
File Size : 40,9 Mb
Release : 2023
Category : Electronic
ISBN : OCLC:1417259403
Author : Arslan Munir,Joonho Kong,Mahmood Azhar Qureshi
Publisher : John Wiley & Sons
Page : 308 pages
File Size : 45,9 Mb
Release : 2023-10-16
Category : Computers
ISBN : 9781394171903
Accelerators for Convolutional Neural Networks Comprehensive and thorough resource exploring different types of convolutional neural networks and complementary accelerators Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration. The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models. Later chapters focus on compressive coding for CNNs and the design of dense CNN accelerators. The book also provides directions for future research and development for CNN accelerators. Other sample topics covered in Accelerators for Convolutional Neural Networks include: How to apply arithmetic coding and decoding with range scaling for lossless weight compression for 5-bit CNN weights to deploy CNNs in extremely resource-constrained systems State-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arrays iMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware acceleration Multi-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense accelerator Sparse-PE, a multi-threaded and flexible CNN PE core that exploits sparsity in both weights and activation maps, instances of which can be stacked in a spatial grid for engendering sparse CNN accelerators For researchers in AI, computer vision, computer architecture, and embedded systems, along with graduate and senior undergraduate students in related programs of study, Accelerators for Convolutional Neural Networks is an essential resource to understanding the many facets of the subject and relevant applications.
Author : Sandeep Saini,Kusum Lata,G.R. Sinha
Publisher : CRC Press
Page : 329 pages
File Size : 55,6 Mb
Release : 2021-12-30
Category : Technology & Engineering
ISBN : 9781000523812
Provides the details of state-of-the-art machine learning methods used in VLSI Design. Discusses hardware implementation and device modeling pertaining to machine learning algorithms. Explores machine learning for various VLSI architectures and reconfigurable computing. Illustrate latest techniques for device size and feature optimization. Highlight latest case studies and reviews of the methods used for hardware implementation.
Author : Sudeep Pasricha,Muhammad Shafique
Publisher : Springer Nature
Page : 418 pages
File Size : 44,5 Mb
Release : 2023-11-01
Category : Technology & Engineering
ISBN : 9783031195686
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.
Author : Joao Gama,Sepideh Pashami,Albert Bifet,Moamar Sayed-Mouchawe,Holger Fröning,Franz Pernkopf,Gregor Schiele,Michaela Blott
Publisher : Springer Nature
Page : 317 pages
File Size : 50,6 Mb
Release : 2021-01-09
Category : Computers
ISBN : 9783030667702
This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
Author : Shai Avidan,Gabriel Brostow,Moustapha Cissé,Giovanni Maria Farinella,Tal Hassner
Publisher : Springer Nature
Page : 813 pages
File Size : 44,5 Mb
Release : 2022-10-22
Category : Computers
ISBN : 9783031197758
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Author : Vivienne Sze,Yu-Hsin Chen,Tien-Ju Yang,Joel S. Emer
Publisher : Springer Nature
Page : 254 pages
File Size : 45,9 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017667
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 : Tushar Krishna,Hyoukjun Kwon,Angshuman Parashar,Michael Pellauer,Ananda Samajdar
Publisher : Springer Nature
Page : 146 pages
File Size : 48,8 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017674
This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.
Author : Jeffrey Nichols,Arthur ‘Barney’ Maccabe,James Nutaro,Swaroop Pophale,Pravallika Devineni,Theresa Ahearn,Becky Verastegui
Publisher : Springer Nature
Page : 474 pages
File Size : 44,5 Mb
Release : 2022-03-09
Category : Computers
ISBN : 9783030964986
This book constitutes the revised selected papers of the 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, held in Oak Ridge, TN, USA*, in October 2021. The 33 full papers and 3 short papers presented were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; advanced computing applications: use cases that combine multiple aspects of data and modeling; advanced computing systems and software: connecting instruments from edge to supercomputers; deploying advanced computing platforms: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.
Author : Phan Cong Vinh,Abdur Rakib
Publisher : Springer Nature
Page : 347 pages
File Size : 49,8 Mb
Release : 2022-01-06
Category : Computers
ISBN : 9783030931797
This book constitutes the refereed post-conference proceedings of the International Conference on Context-Aware Systems and Applications, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 25 revised full papers presented were carefully selected from 52 submissions. The papers cover a wide spectrum of modern approaches and techniques for smart computing systems and their applications.
Author : Shmuel Winograd
Publisher : SIAM
Page : 96 pages
File Size : 50,7 Mb
Release : 1980-01-01
Category : Mathematics
ISBN : 1611970369
Focuses on finding the minimum number of arithmetic operations needed to perform the computation and on finding a better algorithm when improvement is possible. The author concentrates on that class of problems concerned with computing a system of bilinear forms. Results that lead to applications in the area of signal processing are emphasized, since (1) even a modest reduction in the execution time of signal processing problems could have practical significance; (2) results in this area are relatively new and are scattered in journal articles; and (3) this emphasis indicates the flavor of complexity of computation.
Author : Brandon Reagen,Robert Adolf,Paul Whatmough,Gu-Yeon Wei,David Brooks
Publisher : Springer Nature
Page : 109 pages
File Size : 53,5 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031017568
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Author : Scott Hauck,André DeHon
Publisher : Elsevier
Page : 944 pages
File Size : 44,9 Mb
Release : 2010-07-26
Category : Computers
ISBN : 0080556019
Reconfigurable Computing marks a revolutionary and hot topic that bridges the gap between the separate worlds of hardware and software design— the key feature of reconfigurable computing is its groundbreaking ability to perform computations in hardware to increase performance while retaining the flexibility of a software solution. Reconfigurable computers serve as affordable, fast, and accurate tools for developing designs ranging from single chip architectures to multi-chip and embedded systems. Scott Hauck and Andre DeHon have assembled a group of the key experts in the fields of both hardware and software computing to provide an introduction to the entire range of issues relating to reconfigurable computing. FPGAs (field programmable gate arrays) act as the “computing vehicles to implement this powerful technology. Readers will be guided into adopting a completely new way of handling existing design concerns and be able to make use of the vast opportunities possible with reconfigurable logic in this rapidly evolving field. Designed for both hardware and software programmers Views of reconfigurable programming beyond standard programming languages Broad set of case studies demonstrating how to use FPGAs in novel and efficient ways
Author : Pete Warden,Daniel Situnayake
Publisher : O'Reilly Media
Page : 504 pages
File Size : 51,5 Mb
Release : 2019-12-16
Category : Computers
ISBN : 9781492052012
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
Author : Amos R. Omondi,Jagath C. Rajapakse
Publisher : Springer Science & Business Media
Page : 365 pages
File Size : 44,5 Mb
Release : 2006-10-04
Category : Technology & Engineering
ISBN : 9780387284873
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.