HAp-FT: A Hybrid Approximate Fault Tolerance Framework for DNN Accelerator

Nowadays, as Deep Neural Networks (DNNs) become ubiquitous in mission-critical domains (e.g., automatic driving systems), ensuring the reliable execution of domain-specific DNN accelerators in the presence of hardware faults is increasingly essential. However, recent fault tolerance attempts either suffer from expensive performance overhead or are limited in scalability by specific fault modes. This paper proposes HAp-FT, a Hybrid Approximate fault Tolerance framework that integrates both proactive error alleviation and efficient error detection design philosophies. First, considering the vertical accumulation execution paradigm of the systolic array, HAp-FT proactively transfers the reliability risk of vulnerable weight to neighboring Processing Elements (PEs) for advance fault effect alleviation. Then, leveraging the inter-filter similarity characteristics, HAp-FT remaps similar filters to adjacent columns of PEs to achieve real-time intragroup approximate error detection. Experimental results exhibit that HAp-FT can recover 98.38%95.74% accuracy degradation incurred by transient/permanent hardware faults while only introducing 0.18% performance overhead, 2.69% extra area, and 2.38% energy overhead. Moreover, to satisfy the reliability requirements of various application scenarios, HAp-FT is also portable to systolic arrays under all dataflow strategies and adaptable quantized models.