A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction

Deep neural networks (DNNs) have seen growing adoption in safety-critical domains. Unfortunately, they are also subject to unexpected failures due to hardware transient faults (soft errors). Traditional fault tolerance techniques require significant implementation efforts and/or incur major performance overheads. This work introduces Ranger, a low-cost fault corrector that can directly correct the faulty prediction output due to transient faults without re-computation. This research laid the foundations of improving the fault tolerance of DNN applications under hardware transient faults and it has influenced subsequent work in the area, both in academia and industry.