English(EN) An Empirical Fault Vulnerability Exploration of ReRAM-based Process-in-Memory CNN Accelerators

研究论文探讨基于ReRAM的PIM CNN加速器的故障脆弱性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

一篇新研究论文探讨了基于ReRAM的内存处理（PIM）加速器对故障的脆弱性，这些故障会降低深度卷积神经网络（CNN）的准确性。该研究开发了一个故障注入框架，用于分析映射到这些加速器的CNN参数上的“卡在高”（SaH）和“卡在低”（SaL）故障的影响。研究结果表明，故障脆弱性受层类型、参数位置和故障特性的影响，其中SaL故障比SaH故障对分类准确性的损害更大。 AI

影响强调了AI专用硬件潜在的可靠性问题，影响CNN在关键系统的部署。

排序理由在arXiv上发表的学术论文，详细介绍了对硬件脆弱性的技术探索。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Aniseh Dorostkar, Hamed Farbeh, Hamid R. Zarandi · 2026-10-07 04:00

基于ReRAM的内存内计算CNN加速器的经验性故障漏洞探索

arXiv:2610.07029v1 Announce Type: cross Abstract: Resistive random-access memory (ReRAM)-based Processing-in-Memory (PIM) accelerator is a promising platform for processing massively memory intensive matrix-vector multiplications of neural networks in parallel domain, due to its …

本文如何被排名

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基于ReRAM的内存内计算CNN加速器的经验性故障漏洞探索

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