AI边缘设备内存计算中的电路设计挑战

2019 IEEE 13th International Conference on ASIC (ASICON) Pub Date : 2019-10-01 DOI:10.1109/ASICON47005.2019.8983627

Xin Si, H. Qian, Meng-Fan Chang, Cheng-Xin Xue, Jian-Wei Su, Zhixiao Zhang, Sih-Han Li, S. Sheu, Heng-Yuan Lee, Ping-Cheng Chen, Huaqiang Wu

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引用次数: 5

摘要

内存计算(CIM)结构旨在克服内存瓶颈，提高人工智能(AI)边缘设备的能源效率。在本文中，我们回顾了用于物联网和人工智能应用程序的CIM宏开发的最新趋势。我们还研究了AI边缘设备基于SRAM和非易失性存储器的cim开发的最新进展，以及电路设计中涉及的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Circuit Design Challenges in Computing-in-Memory for AI Edge Devices

Computing-in-memory (CIM) structures are meant to overcome the memory bottleneck and improve energy efficiency for artificial intelligence (AI) edge devices. In this article, we review recent trends in the development of CIM macros for the Internet of Things and AI applications. We also look at recent advances in the development of CIMs based on SRAM and nonvolatile memory for AI edge devices as well as the challenges involved in circuit design.

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2019 IEEE 13th International Conference on ASIC (ASICON)

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