书目名称 | Deep In-memory Architectures for Machine Learning |
编辑 | Mingu Kang,Sujan Gonugondla,Naresh R. Shanbhag |
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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 |
图书封面 |  |
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 |
关键词 | machine learning in hardware; analog in-memory architectures; Deep In-memory Architecture; Shannon-insp |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-030-35971-3 |
isbn_softcover | 978-3-030-35973-7 |
isbn_ebook | 978-3-030-35971-3 |
copyright | Springer Nature Switzerland AG 2020 |