Low-Latency Activation-Regularized Sparse Neural Operators with Distillation Assistance Towards Real-Time Edge-Deployable Virtual Sensing
The Sparse-Activation-ReLU (SAR) layer is proposed, a single-step alternative that promotes activation sparsity without surrogate-gradient training while remaining compatible with event-based computing and is a step towards energy-efficient virtual sensing.
William Howes, Farid Ahmed, S. Alam
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