Spike-Aware INT8 Execution for Spiking Language Models on Commodity CPUs
Binary spike activations allow a language-model runtime to read only active weight columns and replace multiplications by weight sums. We implement this execution strategy in C++ for an 874M-parameter spike-gated language model. Sparse projections use column-major INT8 weights, integer accumulation, and one scale application per output channel; dense projections retain row-major access and FP32 activations. In a single-thread comparison using an early checkpoint, INT8 achieves 23.31 tokens/s versus 9.82 for FP32, while reducing weight storage from 3355.2 to 1087.4 MiB. A variant using INT4 on dense projections saves a further 17.4% of storage but reduces decode throughput by 46.6%. On an ...
| Authors | Ting Liu |
|---|---|
| Primary category | cs.NE |
| Published | 2 June 2026 |
| Last updated at source | 7 September 2026 |
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