Concurrent Self-testing of Neural Networks Using Uncertainty Fingerprint
Soyed Tuhin Ahmed Mehdi B. tahoori
Neural networks (NNs) are increasingly used in always-on safety-critical
applications deployed on hardware accelerators (NN-HAs) employing various
memory technologies. Reliable continuous operation of NN is essential for
safety-critical applications. During online operation, NNs are susceptible to
single and multiple permanent and soft errors due to factors such as radiation,
aging, and thermal effects. Explicit NN-HA testing methods cannot detect
transient faults during inference, are unsuitable for always-on applications,
and require extensive test vector generation and storage. Therefore, in this
paper, we propose the \emph{uncertainty fingerprint} approach representing the
online fault status of NN. Furthermore, we propose a dual head NN topology
specifically designed to produce uncertainty fingerprints and the primary
prediction of the NN in \emph{a single shot}. During the online operation, by
matching the uncertainty fingerprint, we can concurrently self-test NNs with up
to $100\%$ coverage with a low false positive rate while maintaining a similar
performance of the primary task. Compared to existing works, memory overhead is
reduced by up to $243.7$ MB, multiply and accumulate (MAC) operation is reduced
by up to $10000\times$, and false-positive rates are reduced by up to $89\%$.