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中国物理学会期刊

基于非挥发存储器的存内计算技术

CSTR: 32037.14.aps.71.20220397

Non-volatile memory based in-memory computing technology

CSTR: 32037.14.aps.71.20220397
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  • 通过在基本单元上集成存储和计算功能, 存内计算技术能够显著降低数据搬运规模, 被广泛认为是突破传统冯·诺依曼计算架构性能瓶颈的新型计算范式. 非挥发存储器件兼具非易失特性和存算融合功能, 是实现存内计算的良好功能器件. 本文首先介绍了存内计算范式的基本概念, 包括技术背景和技术特征. 然后综述了用于实现存内计算的非挥发存储器件及其性能特征, 包含传统闪存器件和新型阻变存储器; 进一步介绍了基于非挥发存储器件的存内计算实现方法, 包括存内模拟运算和存内数字运算. 之后综述了非挥发存内计算系统在深度学习硬件加速、类脑计算等领域的潜在应用. 最后, 对非挥发型存内计算技术的未来发展趋势进行了总结和展望.

     

    By integrating the storage and computing functions on the fundamental elements, computing in-memory (CIM) technology is widely considered as a novel computational paradigm that can break the bottleneck of Von Neumann architecture. Nonvolatile memory device is an appropriate hardware implementation approach of CIM, which possess significantly advantages, such as excellent scalability, low consumption, and versatility. In this paper, first we introduce the basic concept of CIM, including the technical background and technical characteristics. Then, we review the traditional and novel nonvolatile memory devices, flash and resistive random access memory (RRAM), used in non-volatile based computing in-memory (nvCIM) system. After that, we explain the operation modes of nvCIM: in-memory analog computing and in-memory digital computing. In addition, the applications of nvCIM are also discussed, including deep learning accelerator, neuromorphic computing, and stateful logic. Finally, we summarize the current research advances in nvCIM and provide an outlook on possible research directions in the future.

     

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