Novel Device for Computing-In Memory

Dear Colleagues,

Today's computing is not limited to designing an application graph of the desired work for available hardware and then starting the execution; it is more application-specific and data-centric. The processor should be able to predict future machine cycles and improve performance through data-centric learning.  This type of architecture requires a high data bus bandwidth and speed. Von-Neumann architecture is widely for flexible design space but limits performance due to data movement between the processor and memory, which depends on memory speed and bandwidth. Computing in memory (CIM) is an alternative processor design technique where processing is carried out inside the memory itself. It attracts manly data-centric applications such as neuromorphic computing, machine learning, and soft computing. CIM may use traditional memories, such as SRAM, DRAM, and Flash, along with emerging devices based on memories, such as Ferroelectric transistor (FeFET), spin-transfer torque magnetic random-access memory (STT-MRAM), and pulse-code modulation  (PCM). Research on CIM focuses on different aspects of computing engine design, such as the device, circuit, architecture, accelerators, processor design, and algorithms. This Special Issue covers all topics related to the full CIM stack: device entry, fabrication, measurement, data analysis circuit design, architecture, algorithm, accelerator design, and applications.

Dr. Nandakishor Yadav
Dr. Ken Choi
Guest Editors

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