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This work presents a posttraining framework that simultaneously hardens analog IMC accelerators against both threats without retraining the model and restores near-baseline accuracy and mitigates the threat of correlation-based power analysis attacks.
The research rigorously investigates how neuromorphic architectures encode and integrate temporal information by conducting a comprehensive ablation study using a hybrid network, and demonstrates that shallow neuromorphic integration effectively maximizes the gains from temporal integration while mitigating the informa...
This work introduces FeatureFormer, a neural performance predictor that incorporates explicit node-wise encodings of FLOPs, parameter counts, and memory proxies within a gated graph attention architecture and presents NNEQ, a new large-scale energy consumption dataset that enables unified evaluation of latency and ener...
A novel and much-refined framework that extends SNN far beyond their current implementation and introduces a significant number of biologically-inspired structural and functional innovations is needed.
This work proposes heterogeneous neural networks that combine spiking neural networks (SNNs) and artificial neural networks (ANNs) at bandwidth-limited regions, such as chip boundaries, where spike-based communication reduces data transfer overhead.
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