Accelerators for convolutional neural networks

Arslan Munir, Joonho Kong, Mahmood Azhar Qureshi

Research output: Book/ReportBook


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.

Original languageEnglish (US)
Number of pages288
ISBN (Electronic)9781394171910
ISBN (Print)9781394171880
StatePublished - Aug 28 2023

ASJC Scopus subject areas

  • General Computer Science


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