Spiking Neural Network on Neuromorphic Hardware for EnergyEfficient Unidimensional SLAM Energyefficient simultaneous localization and mapping (SLAM) is crucial for mobile robots exploring unknown environments. The mammalian brain solves SLAM via a network of specialized neurons, exhibiting asynchronous computations and eventbased communications, with very low energy consumption. We propose a braininspired spiking neural network (SNN) architecture that solves the unidimensional SLAM by introducing spikebased reference frame transformation, visual likelihood computation, and Bayesian inference. We integrated our neuromorphic algorithm to Intelxe2x80x99s Loihi neuromorphic processor, a nonVon Neumann hardware that mimics the brainxe2x80x99s computing paradigms. We performed comparative analyses for accuracy and energyefficiency between our neuromorphic approach and the GMapping algorithm, which is widely used in small environments. Our Loihibased SNN architecture consumes 100 times less energy than GMapping run on a CPU while having comparable accuracy in head direction localization and mapgeneration. These results pave the way for scaling our approach towards activeSLAM alternative solutions for Loihicontrolled autonomous robots.

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.