神經形態晶片的測試壓縮

Abstract

我們提出測試壓縮技術以降低神經形態晶片的測試時間 (測試配置以及測試樣本)。我們的測試壓縮技術包括動態測試壓縮以及靜態測試壓縮。動態測試壓縮利用機器學習來生成測試配置。靜態測試壓縮在雙樣本霍特林T平方測試的顯著水準限制下降低測試長度。在兩個神經形態架構的實驗結果表明，我們提出的技術可以降低90.44%的總測試配置，以及93.47%的總測試長度。我們的運行時間比先前的方法快了十倍以上。我們所提出的技術不受限於神經形態晶片的應用。

We propose test compression techniques to reduce the test time (test configurations and test length) for neuromorphic chips. Our test compression techniques include Dynamic Test Compression (DTC) and Static Test Compression (STC). DTC generates test configurations with machine learning. STC reduces test length under the constraint of the significance level in Two-sample Hotelling’s T-Square Test. Experiments on two neuromorphic architectures show that our proposed techniques can reduce the total test configurations by 90.44% and the total test length by 93.47%, respectively. Our run time is more than 10x faster than the previous method. The proposed techniques are independent of neuromorphic chips’ applications.