Deep In-memory Architectures for Machine Learning

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.

Publication Year: 2020
Imprint: Springer International Publishing
Format: H
Weight (Gram): 453