干扰意识的芯片上培训与模拟深度学习加速器中大规模并行计算的缓解方案
Jaehyeon Kang1, Jongun Won1, Narae Han1
1Department of Material Science & Engineering, Inter-university Semiconductor Research Center (ISRC), Research Institute of Advanced Materials (RIAM), Seoul National University, Seoul, 08826, Republic of Korea.
概括
这项研究量化了在芯片上训练期间模拟内存计算 (AIMC) 突触设备中的干扰. 拟议的缓解方案可以在大型数组中实现准确的深度学习.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 人工智能的人工智能
背景情况:
- 在模拟内存计算 (AIMC) 中的芯片上培训有望减少数据延迟和个性化学习.
- 交叉阵列中的模拟突触器件面临着诸如非统一编程和并行重量更新期间的干扰等挑战.
- 训练期间的干扰不太清楚,这限制了对其对表现的影响的探索.
研究的目的:
- 精确识别和量化6T1C突触器件中的干扰效应.
- 提出和验证用于减轻这些干扰的操作方案.
- 在大型深度学习阵列中评估干扰意识培训的可行性.
主要方法:
- 在6T1C突触器件 (氧化物半导体和电容器) 中干扰机制的表征.
- 开发和实验验证三个操作方案,以减轻干扰的影响.
- 实时,干扰感知训练模拟将突触阵列映射到卷积神经网络 (CNN) 为CIFAR-10数据集.
主要成果:
- 在6T1C设备中的干扰效应被精确地识别和量化,随着设备的扩展而恶化.
- 拟议的操作方案有效地减轻了干扰效应,通过设备阵列测量进行验证.
- 干扰感知训练模拟在CIFAR-10数据集上实现了软件等效的准确性,即使干扰加剧.
结论:
- 澄清干扰机制对于推进AIMC至关重要.
- 拟议的缓解策略为可靠的芯片上培训提供了实际解决方案.
- 这种方法使基于硬件的深度学习能够使用6T1C突触阵列以高精度实现.
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