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Deep in-memory architectures for mac...

Kang, Mingu.

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Deep in-memory architectures for machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep in-memory architectures for machine learning/ by Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.
Author:	Kang, Mingu.
other author:	Gonugondla, Sujan.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	x, 174 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Computer storage devices. -
Online resource:	https://doi.org/10.1007/978-3-030-35971-3
ISBN:	9783030359713

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

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

649652
Computer storage devices.

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

Dewey Class. No.: 004.5

Deep in-memory architectures for machine learning
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245 1 0 $a Deep in-memory architectures for machine learning $h [electronic resource] / $c by Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a x, 174 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
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.
650 0 $a Computer storage devices. $3 649652
650 0 $a Machine learning. $3 533906
650 1 4 $a Circuits and Systems. $3 896527
650 2 4 $a Cyber-physical systems, IoT. $3 3386699
650 2 4 $a Processor Architectures. $3 892680
700 1 $a Gonugondla, Sujan. $3 3447388
700 1 $a Shanbhag, Naresh R. $3 3447389
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856 4 0 $u https://doi.org/10.1007/978-3-030-35971-3
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