Compressed Test Pattern Generation for Deep Neural Networks

Abstract

Deep neural networks (DNNs) have emerged as an effective approach in many artificial intelligence tasks. Several specialized accelerators are often used to enhance DNN's performance and lower their energy costs. However, the presence of faults can drastically impair the performance and accuracy of these accelerators. Usually, many test patterns are required for certain types of faults to reach a target fault coverage, which in turn hence increases the testing overhead and storage cost, particularly for in-field testing. For this reason, compression is typically done after test generation step to reduce the storage cost for the generated test patterns. However, compression is more efficient when considered in an earlier stage. This paper generates the test pattern in a compressed form to require less storage. This is done by generating all test patterns as a linear combination of a set of jointly used test patterns (basis), for which only the coefficients need to be stored. The fault coverage achieved by the generated test patterns is compared to that of the adversarial and randomly generated test images. The experimental results showed that our proposed test pattern outperformed and achieved high fault coverage (up to 99.99%) and a high compression ratio (up to 307.2 $\times$ ).