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SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks.

, , , , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 42 (11): 3815-3828 (November 2023)

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A Comprehensive Analysis on Adversarial Robustness of Spiking Neural Networks., , , , , and . IJCNN, page 1-8. IEEE, (2019)Semantic driven hierarchical learning for energy-efficient image classification., and . DATE, page 1582-1587. IEEE, (2017)Learning to Recognize Actions from Limited Training Examples Using a Recurrent Spiking Neural Model., and . CoRR, (2017)Attention Tree: Learning Hierarchies of Visual Features for Large-Scale Image Recognition., and . CoRR, (2016)Tree-CNN: A Deep Convolutional Neural Network for Lifelong Learning., , and . CoRR, (2018)STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition., , and . CoRR, (2017)FALCON: Feature Driven Selective Classification for Energy-Efficient Image Recognition., , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 36 (12): 2017-2029 (2017)Synthesizing Images from Spatio-Temporal Representations using Spike-based Backpropagation., , and . CoRR, (2019)Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks., , and . IEEE Access, (2019)Beyond classification: directly training spiking neural networks for semantic segmentation., , and . Neuromorph. Comput. Eng., 2 (4): 44015 (December 2022)