A modular framework decomposes Transformer nonlinearities into spike-compatible primitives realized via LIF population coding and bit-shift scaling, supporting Softmax, SiLU, and normalization with under 1% accuracy drop in LLMs.
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Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers
A modular framework decomposes Transformer nonlinearities into spike-compatible primitives realized via LIF population coding and bit-shift scaling, supporting Softmax, SiLU, and normalization with under 1% accuracy drop in LLMs.