In-Memory Computing: Progress, Critical Technologies and Applications

Dear Colleagues,

In-memory computing has emerged as a transformative paradigm that addresses the limitations of traditional von Neumann architectures, where data transfer between memory and processor leads to performance bottlenecks and high energy consumption. By performing computation directly within memory units, this approach enhances computational efficiency, data throughput, and energy optimization in a wide range of data intensive applications.

The continuous development of materials, architectures, and device technologies such as memristors, phase change memories, and resistive random access memory has accelerated the realization of practical in memory systems. These advancements are driving progress across artificial intelligence, big data analytics, and edge computing, contributing to higher processing speed and reduced power usage.

Critical challenges remain in achieving scalability, reliability, and compatibility with existing hardware and software infrastructures. This Special Issue seeks contributions that explore fundamental principles, innovative architectures, circuits, algorithm hardware co design, and real world implementations. Topics of interest include materials innovation, memory cell design, array optimization, neuromorphic architectures, and emerging applications that demonstrate the potential of in memory computing to redefine efficiency and intelligence in modern information technologies.

Dr. Wang Kang
Guest Editor

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