One-Shot Online Testing of Deep Neural Networks Based on Distribution
  Shift Detection
Soyed Tuhin Ahmed Mehdi B. Tahoori
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. To
accelerate the most common operations (matrix-vector multiplication) in NNs,
hardware accelerator architectures such as computation-in-memory (CiM) with
non-volatile memristive crossbars are utilized. Although they offer benefits
such as power efficiency, parallelism, and nonvolatility, they suffer from
various faults and 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 required. In this paper, we propose a \emph{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
$10000\times$.