Accelerators for Convolutional Neural Networks

By:

Material type: Text

TextPublication details: New Jersey Wiley 2024Description: xvi, 288pISBN:

9781394171880

Subject(s):

DDC classification:

MUN/A 006.31

Summary: 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.

List(s) this item appears in: New Arrivals IIIT Kottayam Library

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Reference	IIIT Kottayam Central Library Reference	Reference	006.31 MUN/A (Browse shelf(Opens below))	Not For Loan		2243

Total holds: 0

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.

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