  Resistive random-access memory (ReRAM) is a promising technology for
designing hardware accelerators for deep neural network (DNN) inferencing.
However, stochastic noise in ReRAM crossbars can degrade the DNN inferencing
accuracy. We propose the design and optimization of a high-performance,
area-and energy-efficient ReRAM-based hardware accelerator to achieve robust
DNN inferencing in the presence of stochastic noise. We make two key technical
contributions. First, we propose a stochastic-noise-aware training method,
referred to as ReSNA, to improve the accuracy of DNN inferencing on ReRAM
crossbars with stochastic noise. Second, we propose an information-theoretic
algorithm, referred to as CF-MESMO, to identify the Pareto set of solutions to
trade-off multiple objectives, including inferencing accuracy, area overhead,
execution time, and energy consumption. The main challenge in this context is
that executing the ReSNA method to evaluate each candidate ReRAM design is
prohibitive. To address this challenge, we utilize the continuous-fidelity
evaluation of ReRAM designs associated with prohibitive high computation cost
by varying the number of training epochs to trade-off accuracy and cost.
CF-MESMO iteratively selects the candidate ReRAM design and fidelity pair that
maximizes the information gained per unit computation cost about the optimal
Pareto front. Our experiments on benchmark DNNs show that the proposed
algorithms efficiently uncover high-quality Pareto fronts. On average, ReSNA
achieves 2.57% inferencing accuracy improvement for ResNet20 on the CIFAR-10
dataset with respect to the baseline configuration. Moreover, CF-MESMO
algorithm achieves 90.91% reduction in computation cost compared to the popular
multi-objective optimization algorithm NSGA-II to reach the best solution from
NSGA-II.
