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Deep in-memory architectures for mac...

Gonugondla, Sujan.

Deep in-memory architectures for machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep in-memory architectures for machine learningby Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.
作者:	Kang, Mingu.
其他作者:	Gonugondla, Sujan.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	x, 174 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Computer storage devices.
電子資源:	https://doi.org/10.1007/978-3-030-35971-3
ISBN:	9783030359713$q(electronic bk.)

Deep in-memory architectures for machine learning
Kang, Mingu.

Deep in-memory architectures for machine learning[electronic resource] /by Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag. - Cham :Springer International Publishing :2020. - x, 174 p. :ill., digital ;24 cm.

Introduction -- The Deep In-memory Architecture (DIMA) -- DIMA Prototype Integrated Circuits -- A Variation-Tolerant DIMA via On-Chip Training -- Mapping Inference Algorithms to DIMA -- PROMISE: A DIMA-based Accelerator -- Future Prospects -- Index.

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.

ISBN: 9783030359713$q(electronic bk.)

Standard No.: 10.1007/978-3-030-35971-3doiSubjects--Topical Terms:

202780
Computer storage devices.

LC Class. No.: TK7895.M4 / K364 2020

Dewey Class. No.: 004.5

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520 $a 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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