One-Shot Online Testing of Deep Neural Networks Based on Distribution Shift Detection

Neural networks (NNs) are capable of learning complex patterns and relationships in data to make predictions with high accuracy, making them useful for various tasks. However, NNs are both computation-intensive and memory-intensive methods, making them challenging for edge applications. Hence, hardware AI accelerators architectures based on computation-in-memory (CiM) architectures with nonvolatile memristive crossbars are gaining attention. Although memristive devices offer benefits, such as power efficiency, parallelism, and nonvolatility, they suffer from nonidealities, leading to various faults and parameter variations, both during manufacturing and lifetime operations. This can lead to faulty computations and, in turn, degradation of post-mapping inference accuracy, which is unacceptable for many applications, including safety-critical applications. Therefore, proper testing of NN hardware accelerators is of utmost importance for functional safety. In this article, we propose a one-shot testing approach that can test NNs accelerated on memristive crossbars with only one test vector, making it very suitable for online testing applications. Our approach can consistently achieve 100% fault coverage across several large topologies with up to 201 layers and challenging tasks like semantic segmentation. Nevertheless, compared to existing methods, the fault coverage is improved by up to 24%, the memory overhead is only 0.0123 MB, a reduction of up to $19980\times $ and the number of test vectors is reduced by $10000\times $ .