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Deep In-memory Architectures for Machine Learning

  • Mingu Kang
  • Sujan Gonugondla
  • Naresh R. Shanbhag
Book
  • 1.5k Downloads

Table of contents

  1. Front Matter
    Pages i-x
  2. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 1-6
  3. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 7-47
  4. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 49-79
  5. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 81-100
  6. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 101-137
  7. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 139-160
  8. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages 161-162
  9. Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag
    Pages C1-C1
  10. Back Matter
    Pages 163-174

About this book

Introduction

This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy-latency-accuracy trade-offs. From first principles to lab prototypes, this book provides a comprehensive view of this emerging topic for both the practicing engineer in industry and the researcher in academia. The book is a journey into the exciting world of AI systems in hardware.


  • Describes deep in-memory architectures for AI systems from first principles, covering both circuit design and architectures;
  • Discusses how DIMAs pushes the limits of energy-delay product of decision-making machines via its intrinsic energy-SNR trade-off;
  • Offers readers a unique Shannon-inspired perspective to understand the system-level energy-accuracy trade-off and robustness in such architectures;
  • Illustrates principles and design methods via case studies of actual integrated circuit prototypes with measured results in the laboratory;
  • Presents DIMA's various models to evaluate DIMA's decision-making accuracy, energy, and latency trade-offs with various design parameter.

Keywords

machine learning in hardware analog in-memory architectures Deep In-memory Architecture Shannon-inspired architecture energy-latency-accuracy trade-offs in AI

Authors and affiliations

  • Mingu Kang
    • 1
  • Sujan Gonugondla
    • 2
  • Naresh R. Shanbhag
    • 3
  1. 1.IBM Thomas J. Watson Research CenterYorktown HeightsUSA
  2. 2.University of Illinois at Urbana-ChampaignUrbanaUSA
  3. 3.University of Illinois at Urbana-ChampaignUrbanaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-35971-3
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-35970-6
  • Online ISBN 978-3-030-35971-3
  • Buy this book on publisher's site