When applied to artificial intelligence edge devices, the conventionally von Neumann computing architecture imposes numerous challenges (e.g., improving the energy efficiency), due to the memory-wall bottleneck involving the frequent movement of data between the memory and the processing elements (PE). Computing-in-memory (CIM) is a promising candidate approach to breaking through this so-called memory wall bottleneck. SRAM cells provide unlimited endurance and compatibility with state-of-the-art logic processes. This paper outlines the background, trends, and challenges involved in the further development of SRAM-CIM macros. This paper also reviews recent silicon-verified SRAM-CIM macros designed for logic and multiplication-accumulation (MAC) operations.

中文翻译：

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基于SRAM的AI Edge设备内存计算的挑战和趋势

当应用于人工智能边缘设备时，由于涉及内存和处理单元（PE）之间频繁数据移动的内存壁瓶颈，传统上的冯·诺依曼（von Neumann）计算架构会带来许多挑战（例如，提高能源效率）。内存计算（CIM）是突破这一所谓的内存壁瓶颈的一种有前途的候选方法。SRAM单元具有无限的耐用性，并且与最新的逻辑过程兼容。本文概述了SRAM-CIM宏的进一步开发所涉及的背景，趋势和挑战。本文还回顾了最近为逻辑和乘法累加（MAC）操作而设计的经过硅验证的SRAM-CIM宏。