首款专为动态视觉传感器设计的低延迟图神经网络处理器，实现微秒级推理。
ETHEREAL: A 25.6-$μ$s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge
- 采用邻域并行样条卷积引擎与分层缓存架构处理稀疏事件流。
- 在VGA分辨率下端到端推理延迟仅25.6微秒，能效1.6微焦/次。
- 适合边缘实时视觉应用，如自动驾驶、工业检测等低延迟场景。
动态视觉传感器（DVS）以微秒级时间分辨率生成事件，是实现边缘视觉应用亚毫秒延迟目标的理想选择。然而，其产生的稀疏时空事件流需要新型算法与硬件协同支持。尽管事件驱动图神经网络（EV-GNN）在准确性和效率上表现出色，但目前尚无专用硬件可高效应对其密集规则计算与稀疏不规则内存访问的混合需求。为此，本文提出ETHEREAL，首个专用于EV-GNN的处理器芯片，通过邻域并行样条卷积引擎与分裂式2D/3D存储层次结构，引入新颖的时空事件缓存机制，成功填补该空白。实测结果表明，在最先进的DAGr-GNN工作负载与VGA分辨率（640×480像素）的DSEC数据集上，系统端到端事件推理延迟仅为25.6微秒，能效达1.6微焦/次。
原文摘要 · Abstract (English)
Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with a $μ$s-level time resolution. However, using DVS front ends also calls for novel algorithm/hardware back ends capable of efficiently handling streams of sparse spatiotemporal events. While event-driven graph neural networks (EV-GNNs) have emerged as a solution on the algorithmic side that is both accurate and efficient, there is no dedicated hardware to date capable of efficiently supporting their mixed requirements of dense-regular compute operations and sparse-irregular memory accesses. We therefore introduce ETHEREAL, the first EV-GNN processor chip, capable of bridging this gap by means of a neighbor-parallel spline-convolution engine combined with a split-2D/3D memory hierarchy that introduces a novel spatiotemporal event-caching mechanism. Measurement results demonstrate a 25.6$μ$s latency and a 1.6$μ$J energy per end-to-end event-wise inference on the state-of-the art DAGr-GNN workload and VGA-resolution (640x480 pixels) DSEC dataset.
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