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 possible response and forfeiting the benefits of elastic inference. To address these challenges, we propose ELSA, a near-SRAM dataflow architecture that realizes true elastic inference through a fine-grained spine/token-wise pipeline and hardware optimizations tailored to SNNs. ELSA forwards each spine/token immediately upon production, forming a continuous streaming pipeline that substantially reduces the latency to the first response. To enhance this lightweight execution, ELSA introduces a bundled address event representation protocol to lower communication traffic of network-on-chip (NoC), and leverages mini-batch spiking Gustavson-product to cut memory access and exploit inherent sparsity. Combined with mapping and scheduling optimizations, ELSA achieves efficient, event-driven computation without compromising accuracy. Experiments show that SNNs can outperform quantized artificial neural networks (QANNs) while maintaining on-par accuracy. For a 4-bit ResNet-50, ELSA achieves 3.4$\times$ speedup and 13.6$\times$ higher energy efficiency over the SOTA QANN accelerator (ANT), and 2.9$\times$ speedup and 22.1$\times$ energy efficiency gains over the SOTA SNN accelerator (PAICORE).

翻译：脉冲神经网络（SNN）利用事件驱动和仅加法计算，显著提升了智能计算的效率。SNN的一个关键时序特性——弹性推理，允许输出逐步产生，从而能够比完整评估更早地响应显著输入。然而，现有的SNN专用加速器无法利用这一特性。逐层设计的加速器仅在所有层计算完成后才产生输出，而逐时间步设计的加速器则依赖于粗粒度的逐层流水线，要求同步层内所有脊/标记。这种阻碍使得结果无法被立即转发，延迟了最早可能的响应，并放弃了弹性推理的优势。为解决这些挑战，我们提出了ELSA，一种近SRAM数据流架构，通过细粒度的脊/标记级流水线和针对SNN定制的硬件优化，实现了真正的弹性推理。ELSA在产生每个脊/标记时立即将其转发，形成连续流式流水线，显著降低了首次响应的延迟。为增强这种轻量级执行，ELSA引入了一种捆绑地址事件表示协议，以降低片上网络（NoC）的通信流量，并利用小批量脉冲Gustavson积来减少内存访问并利用固有的稀疏性。结合映射与调度优化，ELSA在保持精度不变的前提下实现了高效的事件驱动计算。实验表明，SNN能在保持同等精度的同时超越量化人工神经网络（QANN）。对于4比特ResNet-50，ELSA在速度上比最先进的QANN加速器（ANT）提升3.4倍，能效提升13.6倍；相比最先进的SNN加速器（PAICORE），速度提升2.9倍，能效提升22.1倍。