Deep In-memory Architectures for Machine Learning
| Author | : Mingu Kang |
| Publisher | : Springer Nature |
| Total Pages | : 181 |
| Release | : 2020-01-30 |
| ISBN-10 | : 9783030359713 |
| ISBN-13 | : 3030359719 |
| Rating | : 4/5 (719 Downloads) |
Book Synopsis Deep In-memory Architectures for Machine Learning by : Mingu Kang
Download or read book Deep In-memory Architectures for Machine Learning written by Mingu Kang and published by Springer Nature. This book was released on 2020-01-30 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.