Poster: Profiling Event Vision Processing on Edge Devices

As RGB camera resolutions and frame-rates improve, their increased energy requirements make it challenging to deploy fast, efficient, and low-power applications on edge devices. Newer classes of sensors, such as the biologically inspired neuromorphic event-based camera, capture only changes in light intensity per-pixel to achieve operational superiority in sensing latency (O(μs)), energy consumption (O(mW)), high dynamic range (140dB), and task accuracy such as in object tracking, over traditional RGB camera streams. However, highly dynamic scenes can yield an event rate of up to 12MEvents/second, the processing of which could overwhelm resource-constrained edge devices. Efficient processing of high volumes of event data is crucial for ultra-fast machine vision on edge devices. In this poster, we present a profiler that processes simulated event streams from RGB videos into 6 variants of framed representations for DNN inference on an NVIDIA Jetson Orin AGX, a representative edge device. The profiler evaluates the trade-offs between the volume of events evaluated, the quality of the processed event representation, and processing time to present the design choices available to an edge-scale event camera-based application observing the same RGB scenes. We believe that this analysis opens up the exploration of novel system designs for real-time low-power event vision on edge devices.