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Titlebook: Deep In-memory Architectures for Machine Learning; Mingu Kang,Sujan Gonugondla,Naresh R. Shanbhag Book 2020 Springer Nature Switzerland AG

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書目名稱Deep In-memory Architectures for Machine Learning
編輯Mingu Kang,Sujan Gonugondla,Naresh R. Shanbhag
視頻videohttp://file.papertrans.cn/265/264559/264559.mp4
概述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-ma
圖書封面Titlebook: Deep In-memory Architectures for Machine Learning;  Mingu Kang,Sujan Gonugondla,Naresh R. Shanbhag Book 2020 Springer Nature Switzerland AG
描述.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..
出版日期Book 2020
關(guān)鍵詞machine learning in hardware; analog in-memory architectures; Deep In-memory Architecture; Shannon-insp
版次1
doihttps://doi.org/10.1007/978-3-030-35971-3
isbn_softcover978-3-030-35973-7
isbn_ebook978-3-030-35971-3
copyrightSpringer Nature Switzerland AG 2020
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DIMA Prototype Integrated Circuits,is a multi-functional DIMA that implements four different machine learning algorithms—the support vector machine (SVM), template matching (TM), .-nearest neighbor (.-NN), and matched filter (MF), thereby demonstrating DIMA’s versatility. The second IC implements the random forest (RF) algorithm whic
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PROMISE: A DIMA-Based Accelerator,nts a DIMA-based accelerator called PROMISE, which realizes a high level of programmability for diverse ML algorithms without noticeably losing the efficiency of mixed-signal accelerators for specific ML algorithms. PROMISE exposes instruction set mechanisms that allow software control over energy-v
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Hasso Plattner,Christoph Meinel,Larry Leiferficiency of mixed-signal accelerators for specific ML algorithms. PROMISE exposes instruction set mechanisms that allow software control over energy-vs-accuracy trade-offs, and supports compilation of high-level languages down to the hardware.
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Book 2020y 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..
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