| 书目名称 | Deep In-memory Architectures for Machine Learning |
| 编辑 | Mingu Kang,Sujan Gonugondla,Naresh R. Shanbhag |
| 视频video | http://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 |
| 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 |