用于使用垂直NAND闪存的耐温度神经网络的动态传递偏差控制
Sung-Ho Park1, Jiseong Im1, Jonghyun Ko1
1Department of Electrical and Computer Engineering and Inter-University Semiconductor Research Center (ISRC), Seoul National University, Seoul, 08826, Korea.
动态通过偏差控制稳定了V-NAND闪存的突触权重,以应对温度变化. 这种方法通过减轻无需额外硬件的导电量转移来提高神经网络的准确性,提高神经形态计算可靠性.
科学领域:
- 固态物理 固态物理
- 计算机工程 计算机工程
- 人工智能的人工智能
背景情况:
- 垂直NAND (V-NAND) 闪存由于其密度和可扩展性而适合神经形态计算.
- 温度变化导致V-NAND突触重量不稳定,降低神经网络推断准确度.
- 现有的解决方案往往需要重新编程或额外的硬件,限制实际应用.
研究的目的:
- 为V-NAND闪存提议和验证动态传递偏差 (DPB) 控制方案.
- 为了提高基于V-NAND的神经网络的温度弹性.
- 为减轻温度引起的重量变化提供一个硬件效率高的解决方案.
主要方法:
- 通过在读取操作期间对未选中的文字行进行自适应性调整,实现了动态传递偏差 (DPB) 控制方案.
- 开发了一个被动的,适应温度的偏差电路,使用具有相反热特性的MOSFET和V-NAND字符串.
- 通过在商业V-NAND设备上的实验测量和使用CIFAR-10图像分类与VGG-11网络的模拟来验证DPB方案.
主要成果:
- 实验数据显示,随着温度的增加,V-NAND设备中的比特线电流发生了显著的变化.
- DPB方案的模拟表明,在广泛的温度范围内,对准确性降解的显著缓解.
- 在较低的温度下调整通过偏差,与固定偏差操作相比,将分类精度提高了高达10.5%p.
结论:
- 动态通道偏差 (DPB) 控制,数字和电路辅助,有效地弥补温度诱导的V-NAND重量变化.
- 拟议的DPB方案提供了一种轻量级和可扩展的解决方案,用于提高V-NAND闪存在神经形态应用中的温度弹性.
- 这种方法提高了基于V-NAND的神经网络在热应力下的可靠性和准确性.
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