  Emerging deep learning workloads urgently need fast general matrix
multiplication (GEMM). To meet such demand, one of the critical features of
machine-learning-specific accelerators such as NVIDIA Tensor Cores, AMD Matrix
Cores, and Google TPUs is the support of mixed-precision enabled GEMM. For DNN
models, lower-precision FP data formats and computation offer acceptable
correctness but significant performance, area, and memory footprint
improvement. While promising, the mixed-precision computation on error
resilience remains unexplored. To this end, we develop a fault injection
framework that systematically injects fault into the mixed-precision
computation results. We investigate how the faults affect the accuracy of
machine learning applications. Based on the error resilience characteristics,
we offer lightweight error detection and correction solutions that
significantly improve the overall model accuracy if the models experience
hardware faults. The solutions can be efficiently integrated into the
accelerator's pipelines.
