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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

13,280.00₹ 16,935.00₹

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ABOUT THE BOOK

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.

TABLE OF CONTENTS

  1. Efficient Hardware Acceleration for Embedded Machine Learning

    1. Front Matter

      Pages 1-1

    2. Massively Parallel Neural Processing Array (MPNA): A CNN Accelerator for Embedded Systems

      • Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
      Pages 3-24

    3. Photonic NoCs for Energy-Efficient Data-Centric Computing

      • Febin P. Sunny, Asif Mirza, Ishan G. Thakkar, Mahdi Nikdast, Sudeep Pasricha
      Pages 25-61

    4. Low- and Mixed-Precision Inference Accelerators

      • Maarten J. Molendijk, Floran A. M. de Putter, Henk Corporaal
      Pages 63-88

    5. Designing Resource-Efficient Hardware Arithmetic for FPGA-Based Accelerators Leveraging Approximations and Mixed Quantizations

      • Salim Ullah, Siva Satyendra Sahoo, Akash Kumar
      Pages 89-119

    6. Efficient Hardware Acceleration of Emerging Neural Networks for Embedded Machine Learning: An Industry Perspective

      • Arnab Raha, Raymond Sung, Soumendu Ghosh, Praveen Kumar Gupta, Deepak A. Mathaikutty, Umer I. Cheema et al.
      Pages 121-172

  2. Memory Design and Optimization for Embedded Machine Learning

    1. Front Matter

      Pages 173-173

    2. An Off-Chip Memory Access Optimization for Embedded Deep Learning Systems

      • Rachmad Vidya Wicaksana Putra, Muhammad Abdullah Hanif, Muhammad Shafique
      Pages 175-198

    3. In-Memory Computing for AI Accelerators: Challenges and Solutions

      • Gokul Krishnan, Sumit K. Mandal, Chaitali Chakrabarti, Jae-sun Seo, Umit Y. Ogras, Yu Cao
      Pages 199-224

    4. Efficient Deep Learning Using Non-volatile Memory Technology in GPU Architectures

      • Ahmet Inci, Mehmet Meric Isgenc, Diana Marculescu
      Pages 225-252

    5. SoC-GANs: Energy-Efficient Memory Management for System-on-Chip Generative Adversarial Networks

      • Rehan Ahmed, Muhammad Zuhaib Akbar, Muhammad Abdullah Hanif, Muhammad Shafique
      Pages 253-274

    6. Using Approximate DRAM for Enabling Energy-Efficient, High-Performance Deep Neural Network Inference

      • Lois Orosa, Skanda Koppula, Konstantinos Kanellopoulos, A. Giray Yağlıkçı, Onur Mutlu
      Pages 275-314

  3. Efficient Software Design for Embedded Machine Learning

    1. Front Matter

      Pages 315-315

    2. On-Chip DNN Training for Direct Feedback Alignment in FeFET

      • Fan Chen
      Pages 317-335

    3. Platform-Based Design of Embedded Neuromorphic Systems

      • M. L. Varshika, Anup Das
      Pages 337-358

    4. Light Speed Machine Learning Inference on the Edge

      • Febin P. Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
      Pages 359-392

    5. Low-Latency, Energy-Efficient In-DRAM CNN Acceleration with Bit-Parallel Unary Computing

      • Ishan G. Thakkar, Supreeth M. Shivanandamurthy, Sayed Ahmad Salehi
      Pages 393-409



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