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PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search
To address this problem, we propose a performance estimation strategy that reduces the resources for training NNs and increases NAS throughput without jeopardizing accuracy.
Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks
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.
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