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.

Ev-Edge 是一个框架，通过三项关键优化技术提高边缘平台上基于事件的视觉系统的性能，包括将事件流转换为稀疏帧、动态稀疏帧聚合和网络映射。在多种自主导航任务中，Ev-Edge 相较于 NVIDIA Jetson Xavier AGX 平台上全 GPU 实现的单任务执行中具有 1.28 倍至 2.05 倍的延迟改进和 1.23 倍至 2.15 倍的能源改进，也相较于循环调度方法在多任务执行场景下具有 1.43 倍至 1.81 倍的延迟改进。