Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware
Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space models (SSMs) mitigate this through linear attention and fixed-size recurrent states, but their large dense linear projections remain computationally expensive even after quantization. We introduce a method that induces sparse neural activity in heavily quantized linear-attention models with minimal performance loss. Activations below a per-projection trainable threshold ($\pm Δ$) are nullified while preserving crucial outliers, achieving comparable performance to dense models with up to 4$\times$ fewer effective arithmetic operations. Targe...
| Authors | Simon Richter, Ruhai Lin, Jason Yik et al. |
|---|---|
| Primary category | cs.NE |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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