API-based Hardware Fault Simulation for DNN-Accelerators

—Computer hardware experiences random faults in some cases causing program failure. The rate of faults can accumulate to notable effect in computer clusters and warrants consideration for high dependability applications. Deep Neural Networks (DNNs) are relatively robust to hard- ware faults. When designing DNN-accelerators, performance may be gained by optimizing the hardware protection overhead, subject to dependability requirements. We propose a novel method for hardware fault simulation to investigate the end-to-end, hardware fault to DNN output failure probability for common DNN-accelerators. In a proof of concept, speed ups of the order of 10 5 compared to full hardware simulations have been achieved.