  Hardware faults on the regular 2-D computing array of a typical deep learning
accelerator (DLA) can lead to dramatic prediction accuracy loss. Prior
redundancy design approaches typically have each homogeneous redundant
processing element (PE) to mitigate faulty PEs for a limited region of the 2-D
computing array rather than the entire computing array to avoid the excessive
hardware overhead. However, they fail to recover the computing array when the
number of faulty PEs in any region exceeds the number of redundant PEs in the
same region. The mismatch problem deteriorates when the fault injection rate
rises and the faults are unevenly distributed. To address the problem, we
propose a hybrid computing architecture (HyCA) for fault-tolerant DLAs. It has
a set of dot-production processing units (DPPUs) to recompute all the
operations that are mapped to the faulty PEs despite the faulty PE locations.
According to our experiments, HyCA shows significantly higher reliability,
scalability, and performance with less chip area penalty when compared to the
conventional redundancy approaches. Moreover, by taking advantage of the
flexible recomputing, HyCA can also be utilized to scan the entire 2-D
computing array and detect the faulty PEs effectively at runtime.
