Deep In-memory Architectures for Machine Learning
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Beschreibung
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.
- 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-making machines via its intrinsic energy-SNR trade-off;
- Offers readers a unique Shannon-inspired perspective to understand the system-level energy-accuracy trade-off and robustness in such architectures;
- Illustrates principles and design methods via case studies of actual integrated circuit prototypes with measured results inthe laboratory;
- Presents DIMA's various models to evaluate DIMA's decision-making accuracy, energy, and latency trade-offs with various design parameter.
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.
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-making machines via its intrinsic energy-SNR trade-off Offers readers a unique Shannon-inspired perspective to understand the system-level energy-accuracy trade-off and robustness in such architectures Illustrates principles and design methods via case studies of actual integrated circuit prototypes with measured results in the laboratory Presents DIMA's various models to evaluate DIMA's decision-making accuracy, energy, and latency trade-offs with various design parameter
Autor*in
Mingu Kang
Themen in »Deep In-memory Architectures for Machine Learning«
machine learning in hardware analog in-memory architectures Deep In-memory Architecture Shannon-inspired architecture energy-latency-accuracy trade-offs in AI