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.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20802 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0NmIFHPykDUY |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
