Deep In-memory Architectures for Machine Learning [electronic resource] / by Mingu Kang, Sujan Gonugondla, Naresh R. Shanbhag.
By: Kang, Mingu [author.]
.
Contributor(s): Gonugondla, Sujan [author.]
| Shanbhag, Naresh R [author.]
| SpringerLink (Online service)
.
Material type: ![materialTypeLabel](/opac-tmpl/lib/famfamfam/BK.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
![](/opac-tmpl/bootstrap/images/filefind.png)
Introduction -- The Deep In-memory Architecture (DIMA) -- DIMA Prototype Integrated Circuits -- A Variation-Tolerant DIMA via On-Chip Training -- Mapping Inference Algorithms to DIMA -- PROMISE: A DIMA-based Accelerator -- Future Prospects -- Index.
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.
There are no comments for this item.