新技术缓解模拟AI硬件中的保持损耗

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-27 20:41

研究人员开发了抵消模拟内存计算硬件中保持损耗引起的精度下降的方法。通过结合电路级补偿技术和算法重新校准（特别是批量归一化），可以缓解数据随时间衰减的影响。在65 nm CMOS阵列上使用VGG-10和WideResNet-28-10等神经网络模型进行的实验表明，这些组合技术即使在60天后也能将推理精度恢复到接近基线的2-4%。 AI

影响提高了直接在专用硬件上执行的AI计算的长期可靠性和准确性。

排序理由学术论文，详细介绍了针对AI硬件特定问题的 novel 技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Giuseppe Iannaccone · 2026-07-27 20:41

减轻65纳米单多晶浮栅模拟内存计算中遗留损耗对推理精度的影响

We show with experiments and system-level simulations that it is possible to successfully mitigate the impact of retention loss on inference accuracy degradation by using both circuit-level compensation techniques and batch normalization recalibration at the algorithmic level. Ex…

本文如何被排名

报道来源 [1]

减轻65纳米单多晶浮栅模拟内存计算中遗留损耗对推理精度的影响

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