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.

Kang, Mingu

Gonugondla, Sujan

Shanbhag, Naresh R.

Reference

Springer

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.\nFrom the Back Cover\nThis 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.\nDescribes 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.

Supply Chain Integration for Sustainable Advantages

Business & Money

Park, Young Won

Shou, Yongyi

Industries

This book offers a systematic literature review and a set of empirical studies on supply chain integration. Supply chain management is recognized as an essential means for firms to achieve competitive advantages, in which supply chain integration is regarded as the core for operational excellence: shorter lead time, lower inventory, faster delivery, and higher product and service quality. Using data from an international survey of manufacturing firms, an interrelated set of studies that investigate supply chain integration from different perspectives at multiple levels are conducted. Intra-organizational factors such as products, production systems, and socio-technical systems are examined to understand their effects on supply chain integration, indicating that firms need to implement different types of supply chain integration for specific competitive priorities. Then, the outcomes of supply chain integration, including its impact on risk management and sustainability management, are investigated from an inter-organizational perspective. The firm is embedded in a manufacturing network, a global supply chain, or both, and it can use supply chain integration as a strategic action for risks. Sustainability has been a vital issue for manufacturers, and supply chain integration can enhance its success. This book makes significant contributions to the relevant literature on supply chain integration and offers insights to supply chain managers in manufacturing industries. &nbsp;

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Books by Kang, Mingu