Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.

This book presents the latest techniques for characterization, modeling and design for nano-scale non-volatile memory (NVM) devices. Coverage focuses on fundamental NVM device fabrication and characterization, internal state identification of memristic dynamics with physics modeling, NVM circuit design and hybrid NVM memory system design-space optimization. The authors discuss design methodologies for nano-scale NVM devices from a circuits/systems perspective, including the general foundations for the fundamental memristic dynamics in NVM devices. Coverage includes physical modeling, as well as the development of a platform to explore novel hybrid CMOS and NVM circuit and system design. * Offers readers a systematic and comprehensive treatment of emerging nano-scale non-volatile memory (NVM) devices; * Focuses on the internal state of NVM memristic dynamics, novel NVM readout and memory cell circuit design and hybrid NVM memory system optimization; * Provides both theoretical analysis and practical examples to illustrate design methodologies; * Illustrates design and analysis for recent developments in spin-toque-transfer, domain-wall racetrack and memristors.