Edge vision systems combining sensing and embedded processing promise
low-latency, decentralized, and energy-efficient solutions that forgo reliance
on the cloud. As opposed to conventional frame-based vision sensors,
event-based cameras deliver a microsecond-scale temporal resolution with sparse
information encoding, thereby outlining new opportunities for edge vision
systems. However, mainstream algorithms for frame-based vision, which mostly
rely on convolutional neural networks (CNNs), can hardly exploit the advantages
of event-based vision as they are typically optimized for dense matrix-vector
multiplications. While event-driven graph neural networks (GNNs) have recently
emerged as a promising solution for sparse event-based vision, their irregular
structure is a challenge that currently hinders the design of efficient
hardware accelerators. In this paper, we propose EvGNN, the first event-driven
GNN accelerator for low-footprint, ultra-low-latency, and high-accuracy edge
vision with event-based cameras. It relies on three central ideas: (i) directed
dynamic graphs exploiting single-hop nodes with edge-free storage, (ii) event
queues for the efficient identification of local neighbors within a
spatiotemporally decoupled search range, and (iii) a novel layer-parallel
processing scheme enabling the low-latency execution of multi-layer GNNs. We
deployed EvGNN on a Xilinx KV260 Ultrascale+ MPSoC platform and benchmarked it
on the N-CARS dataset for car recognition, demonstrating a classification
accuracy of 87.8% and an average latency per event of 16$\mu$s, thereby
enabling real-time, microsecond-resolution event-based vision at the edge.

该研究论文提出了 EvGNN，这是第一个用于边缘视觉的事件驱动 GNN 加速器，通过利用定向动态图、事件队列和新颖的层并行处理方案，实现了边缘视觉的低内存占用、超低延迟和高准确性，从而实现了实时的微秒级分辨率事件驱动视觉。