Deep In-memory Architectures for Machine Learning - Naresh R. Shanbhag,Sujan Gonugondla,Mingu Kang
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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 har ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Naresh R. Shanbhag, Sujan Gonugondla, Mingu Kang |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Izlaides gads | 2021 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783030359737 |