£54.99
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.
Explainable Machine Learning Models and Architectures
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing
Hardware Technologies for Artificial Intelligence
In-Memory Computing Hardware Accelerators for Data-Intensive Applications
Development and Analysis of Deep Learning Architectures
In-Memory Computing
Artificial Intelligence and Hardware Accelerators
Deep Learning Classifiers with Memristive Networks
Artificial Intelligence Hardware Design
Intelligence in Chip: Integrated Sensors and Memristive Computing
Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design
Neuromorphic Intelligence