ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing
Spiking neural networks (SNNs) exploit event-driven and addition-only computation to substantially improve efficiency for intelligent computation. A key temporal property of SNNs, elastic inference, allows outputs to emerge progressively, enabling responses to salient inputs much earlier than full evaluation. However, existing SNN-specific accelerators cannot capitalize on this property. Layer-by-layer designs emit outputs only after all layers are complete, while time-step-by-time-step designs rely on coarse-grained, layer-wise pipelines that require synchronizing all spines/tokens within a layer. This barrier prevents results from being forwarded immediately, delaying the earliest possi...
| Authors | Kang You, Chen Nie, Lee Jun Yan et al. |
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| Primary category | cs.AR |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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