Accelerators for Convolutional Neural Networks 1st Edition by Arslan Munir, Joonho Kong, Mahmood Azhar Qureshi ISBN 1394171889 9781394171880

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Product details:

ISBN 10: 1394171889

ISBN 13: 9781394171880 

Author: Arslan Munir, Joonho Kong, Mahmood Azhar Qureshi 

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.

Table of contents:

Chapter 1. Introduction
Chapter 2. Overview of Convolutional Neural Networks
Chapter 3. Contemporary Advances in Compressive Coding for CNNs
Chapter 4. Lossless Input Feature Map Compression
Chapter 5. Arithmetic Coding and Decoding for 5-Bit CNN Weights
Chapter 6. Contemporary Dense CNN Accelerators
Chapter 7. iMAC: Image-to-Column and General Matrix Multiplication-Based Dense CNN Accelerator
Chapter 8. NeuroMAX: A Dense CNN Accelerator
Chapter 9. Contemporary Sparse CNN Accelerators
Chapter 10. CNN Accelerator for In Situ Decompression and Convolution of Sparse Input Feature Maps
Chapter 11. Sparse-PE: A Sparse CNN Accelerator
Chapter 12. Phantom: A High-Performance Computational Core for Sparse CNNs
Chapter 13. State-of-the-Art in HW/SW Co-Design and Co-Scheduling for CNN Acceleration
Chapter 14. Hardware/Software Co-Design for CNN Acceleration
Chapter 15. CPU-Accelerator Co-Scheduling for CNN Acceleration
Chapter 16. Conclusions 

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Tags: Arslan Munir, Joonho Kong, Mahmood Azhar Qureshi, Accelerators, Convolutional Neural Networks