内存处理
冯·诺依曼建筑
非常规计算
计算机科学
内存计算
钥匙(锁)
认知计算
计算机体系结构
分布式计算
并行计算
内存管理
半导体存储器
平面存储模型
计算机硬件
认知
神经科学
按示例查询
Web搜索查询
操作系统
搜索引擎
生物
计算机安全
情报检索
作者
Abu Sebastian,Manuel Le Gallo,Riduan Khaddam-Aljameh,Evangelos Eleftheriou
标识
DOI:10.1038/s41565-020-0655-z
摘要
Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing. Hereby certain computational tasks are performed in place in the memory itself by exploiting the physical attributes of the memory devices. Both charge-based and resistance-based memory devices are being explored for in-memory computing. In this Review, we provide a broad overview of the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing. This Review provides an overview of memory devices and the key computational primitives for in-memory computing, and examines the possibilities of applying this computing approach to a wide range of applications.
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