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This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory types and describes how it can be a viable computer architecture in the era of AI and big data. The authors summarize the challenges of AI hardware systems, processing-in-memory (PIM) constraints and approaches to derive system-level requirements for a practical and feasible PIM solution. The presentation focuses on feasible PIM solutions that can be implemented and used in real systems, including architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM).
This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory
Research in Artificial Intelligence (AI) is not new, it has been around since 1950’s. AI resurfaced at that time while Moore’s law was on an aggressive path
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-off
This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are curren