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.

Stochasticity in Neuromorphic Computing: Evaluating Randomness for Improved Performance Several researchers have proposed that random noise in highly nonlinear biological systems helps with learning and information processing. Neuromorphic systems could potentially harness the computational power of such biological systems by utilizing stochasticity to imitate random biological noise. Systems implementing this kind of probabilistic behavior would open up an entirely new realm of potential algorithms and applications to neuromorphic developers. This paper proposes an efficient and reliable stochastic spiking neuromorphic topology that utilizes variations of the membrane capacitance to realize stochastic behavior. We also present analyses of networklevel effects of this stochastic spiking neuron behavior, showing the performance impact on classification and other neuromorphic applications. This networklevel analysis shows that stochastic noise does in fact provide powerful generalization properties that improve performance in emerging neuromorphic systems.