AEStream: Accelerated event-based processing with coroutines

Jens Egholm Pedersen, J. Conradt
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引用次数: 1

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.
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AEStream:使用协程加速基于事件的处理
神经形态传感器模仿生物感觉器官和大脑中的稀疏和基于事件的通信。今天的传感器每秒可以发出数百万个异步事件,这对传统计算机来说是一个挑战。为了避免瓶颈效应,需要应用和改进事件的并发和并行处理。我们提出AEStream:一个在传统计算机上有效地将异步事件从输入流传输到输出的库。AEStream利用称为协程的协作多任务原语并发地处理单个事件,这极大地简化了与基于事件的外设(如基于事件的摄像机和(神经形态的)异步硬件)的集成。我们通过对常规线程机制进行基准测试来探索协同程序在并发设置中的影响,并发现AEStream提供了至少两倍的吞吐量。然后,我们将AEStream应用于GPU上的实时边缘检测任务,并演示了1.3倍的处理速度和5倍的内存操作。
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