Accelerators for Convolutional Neural Networks (Hardcover)

Arslan Munir

ISBN 10: 1394171889 / ISBN 13: 9781394171880

Published by John Wiley & Sons Inc, New York, 2023

About this Item

Hardcover. 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 systemsState-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arraysiMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware accelerationMulti-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense acceleratorSparse-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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Bibliographic Details

• Title: Accelerators for Convolutional Neural Networks (Hardcover)
• Publisher: John Wiley & Sons Inc, New York
• Publication Date: 2023
• Language: English
• ISBN 10: 1394171889
• ISBN 13: 9781394171880
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.

About the Author

ARSLAN MUNIR, PhD, is an Associate Professor in the Department of Computer Science of Kansas State University. He is also the Director of the Intelligent Systems, Computer Architecture, Analytics, and Security (ISCAAS) Laboratory at the university.

JOONHO KONG, PhD, is an Associate Professor in the School of Electronics Engineering College of IT Engineering at Kyungpook National University, South Korea.

MAHMOOD AZHAR QURESHI, PhD, is a Senior IP Logic Design Engineer at Intel Corporation in Santa Clara, California.