Deep In-memory Architectures for Machine Learning

Book details

  • File Size: 30 MB
  • Format: epub
  • Print Length: 174 pages
  • Publisher: Springer; 1st ed. 2020 edition (January 30, 2020)
  • Publication Date: January 30, 2020
  • Sold by: Amazon.com Services LLC
  • Language: English
  • ASIN: B084DLNY1S

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.

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