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SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?, , , , , and . CoRR, (2019)SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks., , , , , and . Microprocess. Microsystems, (February 2023)MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences., , , , , , , , and . CoRR, (2020)X-DNNs: Systematic Cross-Layer Approximations for Energy-Efficient Deep Neural Networks., , , , , and . J. Low Power Electron., 14 (4): 520-534 (2018)Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations., , , , , and . ISLPED, page 27:1-27:6. ACM, (2022)LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor., , , , and . IROS, page 79-86. IEEE, (2022)DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware., , , and . CoRR, (2020)RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks., , , , , and . CoRR, (2022)CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor., , , , and . IJCNN, page 1-10. IEEE, (2021)ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations., , , and . DATE, page 1205-1210. IEEE, (2020)