Representable Matrices: Enabling High Accuracy Analog Computation for Inference of DNNs using Memristors
Representable Matrices: Enabling High Accuracy Analog Computation for Inference of DNNs using Memristors
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DOI：
10.1109/asp-dac47756.2020.9045101
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发表时间：
2019-11
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影响因子：
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通讯作者：
Baogang Zhang;Necati Uysal;Deliang Fan;Rickard Ewetz
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文献类型：
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作者：
Baogang Zhang;Necati Uysal;Deliang Fan;Rickard Ewetz
基于忆阻器技术的模拟计算是加速深度神经网络（DNN）推理阶段的一种很有前途的解决方案。一个基本的问题是将任意矩阵映射到忆阻器交叉杆阵列（MCA），同时最大限度地提高计算精度。最先进的映射技术基于启发式算法，该算法仅保证为两个输入向量生成正确的输出。在本文中，提出了一种旨在为每个输入向量产生正确输出的技术，该技术涉及指定忆阻器电导值和由外围电路实现的比例因子。本文的主要观点是，MCA实现的电导矩阵只需要与目标矩阵成比例。两者之间的缩放因子的选择调节可编程忆阻器电导范围的利用和目标矩阵的可表示性。因此，设置比例因子以平衡精度和值范围误差。此外，提出了一种将电导值转换为状态变量的技术，反之亦然，以处理具有非理想器件特性的忆阻器。与现有技术相比，所提出的映射导致4倍至9倍的小误差。这些改进转化为CIFAR-10上七层卷积神经网络（CNN）的分类准确率从20.5%提高到71.8%。
Analog computing based on memristor technology is a promising solution to accelerating the inference phase of deep neural networks (DNNs). A fundamental problem is to map an arbitrary matrix to a memristor crossbar array (MCA) while maximizing the resulting computational accuracy. The state-of-the-art mapping technique is based on a heuristic that only guarantees to produce the correct output for two input vectors. In this paper, a technique that aims to produce the correct output for every input vector is proposed, which involves specifying the memristor conductance values and a scaling factor realized by the peripheral circuitry. The key insight of the paper is that the conductance matrix realized by an MCA is only required to be proportional to the target matrix. The selection of the scaling factor between the two regulates the utilization of the programmable memristor conductance range and the representability of the target matrix. Consequently, the scaling factor is set to balance precision and value range errors. Moreover, a technique of converting conductance values into state variables and vice versa is proposed to handle memristors with non-ideal device characteristics. Compared with the state-of-the-art technique, the proposed mapping results in 4X-9X smaller errors. The improvements translate into that the classification accuracy of a seven-layer convolutional neural network (CNN) on CIFAR-10 is improved from 20.5% to 71.8%.