AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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A spike-aware gradient-based mixed-precision quantization framework for SNNs that adaptively assigns bit-widths using first-order gradient information, avoiding costly second-order Hessian computations is proposed.
NeuroFlex is the first accelerator to assign every output element independently to ANN or SNN execution mode with zero accuracy loss, and extends integer-exact ANN-SNN equivalence from layers to individual output elements, thereby enabling mode switching with no conversion error.
APEX is presented, a dual-sparsity SNN inference accelerator that integrates the PASC-IF neuron into the LoAS hardware framework, and guarantees mathematical equivalence between the converted SNN and the source ANN, thereby achieving ANN-equivalent accuracy at significantly reduced timesteps.
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