Presentation
Ev-Edge: Efficient Execution of Event-based Vision Algorithms on Commodity Edge Platforms
DescriptionEvent-based vision sensors have demonstrated great promise in applications like autonomous UAVs. However, deploying event-based algorithms on heterogeneous edge platforms is inefficient due to mismatch between irregular nature of event streams and diverse characteristics of algorithms (mixture of spiking and conventional neural networks) on one hand and the underlying hardware platform on the other. We introduce Ev-Edge, a framework that contains three key optimizations to boost performance of event-based vision systems on edge platforms. Ev-Edge achieves 1.28x-2.05x latency and 1.23x-2.15x energy improvements over an all-GPU implementation and 1.42x-1.98x latency improvements over round-robin scheduling methods in multi-task execution scenarios with negligible accuracy loss on the NVIDIA Jetson Xavier platform.
Event Type
Research Manuscript
TimeWednesday, June 262:00pm - 2:15pm PDT
Location3001, 3rd Floor
Autonomous Systems
Autonomous Systems (Automotive, Robotics, Drones)