基于eNVM的智能安全计算系统内存计算

Kejie Huang, Chuyun Qin
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引用次数: 1

摘要

最近人工智能(AI)的突破很大程度上依赖于硬件性能的进步。然而,能源和安全一直是现代计算系统和数据中心设计和管理的主要关注点。新兴的嵌入式非易失性存储器(envm)已经引起了各种应用的广泛关注,以提高计算速度和安全性,同时降低功耗。本文综述并讨论了内存计算技术的几个应用实例,包括深度神经网络(DNN)加速器和神经形态计算电路。此外,我们还探讨了内存计算技术的安全性增强,特别是针对机器学习攻击。还讨论了envm提出的新出现的安全问题和可能的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
eNVM based In-memory Computing for Intelligent and Secure Computing Systems
The recent breakthrough in Artificial Intelligence (AI) largely relies on the advance of hardware performance. However, energy and security have been primary concerns in the design and management of modern computing systems and data centers. The emerging embedded Non-Volatile Memories (eNVMs) have drawn great attention to various applications to improve both computing speed and security with much lower power consumption. In this paper, we review and discuss several application cases of the in-memory computing techniques, including Deep Neural Network (DNN) accelerators and neuromorphic computing circuits. Moreover, we also explore the in-memory computing techniques for the security enhancement, especially for machine learning attack. The emerging security issues raised by eNVMs and possible solutions are also discussed.
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