A Hybrid Computing Architecture for Fault-tolerant Deep Learning Accelerators

Regular 2D computing array is widely utilized for the processing of the major neural network operations in many deep learning accelerators (DLAs). Hardware failures on the array can lead to considerable computing errors and prediction accuracy loss. Prior works proposed to add homogeneous redundant PEs to each row or column of the regular computing array to mitigate faulty PEs, but they may fail to recover the computing array from faults when the number of faulty PEs in a row or column exceeds the number of redundant PEs in the corresponding row or column. The problem gets worse when the faults are not evenly distributed across the computing array. To address the problem, we propose a hybrid computing architecture (HCA) for fault-tolerant DLAs. Instead of adding homogeneous redundant PEs to the regular computing array of DLAs, it has a dot-production processing unit (DPPU) to recompute the operations that are mapped to the faulty PEs concurrently without performance penalty under moderate fault injection. Even under high fault injection, HCA can be degraded smoothly and remains functional. In addition, DPPU exploits the parallelism within each operation and processes the network operations sequentially, so it can tolerate faulty PEs in arbitrary locations and ensures steady performance under distinct fault distributions. According to our experiments, HCA shows significantly higher reliability and performance under various fault injection with comparable chip area penalty compared to the conventional redundancy approaches.