ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing
The article introduces ELSA, a new architecture designed for efficient inference in spiking neural networks (SNNs). ELSA aims to leverage the elastic inference property of SNNs to reduce latency and improve energy efficiency. Experimental results indicate that ELSA outperforms existing quantized artificial neural network accelerators in both speed and energy efficiency.
- ▪ELSA is a near-SRAM dataflow architecture that enables true elastic inference through a fine-grained pipeline.
- ▪The architecture allows immediate forwarding of outputs, significantly reducing the latency to the first response.
- ▪ELSA achieves a 3.4 times speedup and 13.6 times higher energy efficiency compared to the state-of-the-art quantized artificial neural network accelerator.
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Computer Science > Hardware Architecture arXiv:2605.20802 (cs) [Submitted on 20 May 2026] Title:ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing Authors:Kang You, Chen Nie, Lee Jun Yan, Ziling Wei, Cheng Zou, Zekai Xu, Yu Feng, Honglan Jiang, Zhezhi He View a PDF of the paper titled ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing, by Kang You and 8 other authors View PDF HTML (experimental) Abstract: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.
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