AEStream: Accelerated event-based processing with coroutines

TL;DR

AEStream is a library that uses coroutines to efficiently process high-rate event streams from neuromorphic sensors, achieving doubled throughput and faster real-time edge detection on GPUs with fewer memory operations.

Contribution

The paper introduces AEStream, a coroutine-based library that improves concurrent event processing efficiency and simplifies integration with neuromorphic hardware.

Findings

AEStream achieves at least twice the throughput of traditional threading.

AEStream enables 1.3 times faster real-time edge detection on GPUs.

AEStream reduces memory operations by a factor of five.

Abstract

Neuromorphic sensors imitate the sparse and event-based communication seen in biological sensory organs and brains. Today's sensors can emit many millions of asynchronous events per second, which is challenging to process on conventional computers. To avoid bottleneck effects, there is a need to apply and improve concurrent and parallel processing of events. We present AEStream: a library to efficiently stream asynchronous events from inputs to outputs on conventional computers. AEStream leverages cooperative multitasking primitives known as coroutines to concurrently process individual events, which dramatically simplifies the integration with event-based peripherals, such as event-based cameras and (neuromorphic) asynchronous hardware. We explore the effects of coroutines in concurrent settings by benchmarking them against conventional threading mechanisms, and find that AEStream provides at least twice the throughput. We then apply AEStream in a real-time edge detection task on a GPU and demonstrate 1.3 times faster processing with 5 times fewer memory operations.