Deep in-memory architectures for machine learning / Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.

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Deep in-memory architectures for machine learning / Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.

9783030359713 (electronic book)
3030359719 (electronic book)
3030359700
9783030359706

Cham : Springer, 2020.

English

1 online resource

TK7895.M4

004.5

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.

Includes bibliographical references and index.

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Print version: 9783030359706

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