Deep In-memory Architectures for Machine Learning Hardback - 2020
by Kang, Mingu/ Gonugondla, Sujan/ Shanbhag, Naresh R
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- Hardback
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A$29.34
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Details
- Title Deep In-memory Architectures for Machine Learning
- Author Kang, Mingu/ Gonugondla, Sujan/ Shanbhag, Naresh R
- Binding Hardback
- Condition New
- Pages 174
- Volumes 1
- Language ENG
- Publisher Springer Verlag
- Publication date 2020
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # x-3030359700
- ISBN 9783030359706 / 3030359700
- Weight 0.97 lbs (0.44 kg)
- Dimensions 9.21 x 6.14 x 0.5 in (23.39 x 15.60 x 1.27 cm)
- Category Technology & Industrial Arts
- Quantity available 2
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
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From the publisher
From the rear cover
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 inthe laboratory;
- Presents DIMA's various models to evaluate DIMA's decision-making accuracy, energy, and latency trade-offs with various design parameter.