  Fault-aware retraining has emerged as a prominent technique for mitigating
permanent faults in Deep Neural Network (DNN) hardware accelerators. However,
retraining leads to huge overheads, specifically when used for fine-tuning
large DNNs designed for solving complex problems. Moreover, as each fabricated
chip can have a distinct fault pattern, fault-aware retraining is required to
be performed for each chip individually considering its unique fault map, which
further aggravates the problem. To reduce the overall retraining cost, in this
work, we introduce the concept of resilience-driven retraining amount
selection. To realize this concept, we propose a novel framework, Reduce, that,
at first, computes the resilience of the given DNN to faults at different fault
rates and with different amounts of retraining. Then, based on the resilience,
it computes the amount of retraining required for each chip considering its
unique fault map. We demonstrate the effectiveness of our methodology for a
systolic array-based DNN accelerator experiencing permanent faults in the
computational array.
