Ares: A framework for quantifying the resilience of deep neural networks
As the use of deep neural networks continues to grow, so does the fraction of compute cycles devoted to their execution. This has led the CAD and architecture communities to devote considerable at- tention to building DNN hardware. Despite these efforts, the fault tolerance of DNNs has generally been overlooked. This paper is the first to conduct a large-scale, empirical study of DNN resilience. Mo- tivated by the inherent algorithmic resilience of DNNs, we are inter- ested in understanding the relationship between fault rate and model accuracy. To do so, we present Ares: a light-weight, DNN-specific fault injection framework validated within 12% of real hardware. We find that DNN fault tolerance varies by orders of magnitude with respect to model, layer type, and structure.
