Efficient Low Cost Alternative Testing of Analog Crossbar Arrays for Deep Neural Networks
Efficient Low Cost Alternative Testing of Analog Crossbar Arrays for Deep Neural Networks
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DOI：
10.1109/itc50671.2022.00060
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Kwondo Ma;Anurup Saha;C. Amarnath;A. Chatterjee
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文献类型：
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作者：
Kwondo Ma;Anurup Saha;C. Amarnath;A. Chatterjee
模拟交叉阵列最近因其在超低功耗的深度神经网络 (DNN) 计算中的实用性而引起了广泛关注。然而，最近的研究表明，由于制造工艺变异性的影响，导致其功能安全性下降，使用这种交叉阵列实现的 DNN 的性能下降高达 30%。测试这些 DNN 的一种方法是将一组详尽的测试图像应用于每个设备以确定其性能。这是昂贵且耗时的。我们提出了一种替代测试方案，其中将一小部分测试图像应用于每个 DNN，并直接根据网络最终层输出的观察来预测 DNN 的分类精度。这节省了测试成本，同时允许对 DNN 进行分级以提高性能。给出了各种测试用例的实验结果，并显示与使用详尽的测试图像集进行的测试相比，测试效率提高了 10.3 倍。
Analog crossbar arrays have recently attracted significant attention due to their usefulness for deep neural net (DNN) computations with ultra-low power consumption. However, recent studies have shown that DNNs implemented with such crossbar arrays suffer from as high as 30% degradation in performance due to the effects of manufacturing process variability effects resulting in degradation of their functional safety. One way to test these DNNs is to apply an exhaustive set of test images to each device to ascertain its performance. This is expensive and time-consuming. We propose an alternative test scheme in which a small subset of test images is applied to each DNN and the classification accuracy of the DNN is predicted directly from observation of the final layer outputs of the network. This saves test cost while allowing binning of DNNs for performance. Experimental results for a variety of test cases are presented and show test efficiency improvements of 10.3X over testing with the exhaustive test image set.