Event cameras have emerged as a promising sensing modality for autonomous navigation systems, owing to their high temporal resolution, high dynamic range and negligible motion blur. To process the asynchronous temporal event streams from such sensors, recent research has shown that a mix of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) as well as hybrid SNN-ANN algorithms are necessary to achieve high accuracies across a range of perception tasks. However, we observe that executing such workloads on commodity edge platforms which feature heterogeneous processing elements such as CPUs, GPUs and neural accelerators results in inferior performance. This is due to the mismatch between the irregular nature of event streams and diverse characteristics of algorithms on the one hand and the underlying hardware platform on the other. We propose Ev-Edge, a framework that contains three key optimizations to boost the performance of event-based vision systems on edge platforms: (1) An Event2Sparse Frame converter directly transforms raw event streams into sparse frames, enabling the use of sparse libraries with minimal encoding overheads (2) A Dynamic Sparse Frame Aggregator merges sparse frames at runtime by trading off the temporal granularity of events and computational demand thereby improving hardware utilization (3) A Network Mapper maps concurrently executing tasks to different processing elements while also selecting layer precision by considering both compute and communication overheads. On several state-of-art networks for a range of autonomous navigation tasks, Ev-Edge achieves 1.28x-2.05x improvements in latency and 1.23x-2.15x in energy over an all-GPU implementation on the NVIDIA Jetson Xavier AGX platform for single-task execution scenarios. Ev-Edge also achieves 1.43x-1.81x latency improvements over round-robin scheduling methods in multi-task execution scenarios.

翻译：事件相机凭借其高时间分辨率、高动态范围与极低运动模糊特性，已成为自主导航系统极具前景的传感模态。为处理此类传感器产生的异步时间事件流，近期研究表明需融合人工神经网络（ANN）、脉冲神经网络（SNN）及混合SNN-ANN算法，才能在多类感知任务中实现高精度。然而，我们观察到在配备CPU、GPU与神经加速器等异构处理单元的商品化边缘平台上执行此类工作负载时，性能表现不佳。其根源在于事件流的非规则性、算法多样性特征与底层硬件平台之间存在失配。为此，我们提出Ev-Edge框架，包含三项关键优化以提升边缘平台事件驱动视觉系统性能：（1）事件转稀疏帧转换器（Event2Sparse Frame），将原始事件流直接转换为稀疏帧，以最小编码开销实现稀疏库调用；（2）动态稀疏帧聚合器（Dynamic Sparse Frame Aggregator），通过权衡事件时间粒度与计算需求运行时合并稀疏帧，提升硬件利用率；（3）网络映射器（Network Mapper），在考虑计算与通信开销的前提下，将并行执行任务映射至不同处理单元并选择层精度。在面向多类自主导航任务的最优网络架构上，Ev-Edge于NVIDIA Jetson Xavier AGX平台单任务执行场景中，相较纯GPU实现实现1.28倍至2.05倍延迟优化与1.23倍至2.15倍能效提升；在多任务执行场景中，相较轮询调度方法实现1.43倍至1.81倍延迟改进。