Enabling EnergyEfficient and Reliable Neural Network via NeuronLevel Voltage Scaling As the application scope of deep neural networks (DNNs) moves from largescale data centers to smallscale mobile devices, power wall has become one of the most important obstacles. Voltage scaling is a typical technique enables power saving, but it causes reliability and performance challenges. Therefore, an energyefficient and reliable scheme for NNs is required to balance above three aspects according to usersu0027 requirements for excellent user experience. In this paper, we innovatively propose neuronlevel voltage scaling framework called NNAPP to model the impact of supply voltages on NNs from output accuracy (A), power (P), and performance (P) perspectives. We analyze the error propagation in NNs and precisely model the impact of voltage scaling on the final output accuracy at neuronlevel. Multiobjective optimization and clustering method are combined to find the optimal voltage islands. Finally, we conduct experiment to demonstrate the efficacy of the proposed technique.

MultiObjective Optimization for Size and Resilience of Spiking Neural Networks Inspired by the connectivity mechanisms in the brain, neuromorphic computing architectures model Spiking Neural Networks (SNNs) in silicon. As such, neuromorphic architectures are designed and developed with the goal of having small, low power chips that can perform control and machine learning tasks. However, the power consumption of the developed hardware can greatly depend on the size of the network that is being evaluated on the chip. Furthermore, the accuracy of a trained SNN that is evaluated on chip can change due to voltage and current variations in the hardware that perturb the learned weights of the network. While efforts are made on the hardware side to minimize those perturbations, a software based strategy to make the deployed networks more resilient can help further alleviate that issue. In this work, we study Spiking Neural Networks in two neuromorphic architecture implementations with the goal of decreasing their size, while at the same time increasing their resiliency to hardware faults. We leverage an evolutionary algorithm to train the SNNs and propose a multiobjective fitness function to optimize the size and resiliency of the SNN. We demonstrate that this strategy leads to wellperforming, smallsized networks that are more resilient to hardware faults.