论文标题
Aestream：与Coroutines一起加速的基于事件的加速处理
AEStream: Accelerated event-based processing with coroutines
论文作者
论文摘要
神经形态传感器模仿生物感觉器官和大脑中看到的稀疏和基于事件的通信。当今的传感器每秒可能会发出数百万个异步事件，这在传统计算机上进行处理具有挑战性。为避免瓶颈效果，需要应用和改善事件的并行和并行处理。 我们提出Aestream：一个库，可有效地从输入到传统计算机上的输出有效流传输异步事件。 Aestream利用称为Coroutines的合作多任务原始词同时处理单个事件，从而极大地简化了与基于事件的外围设备的集成，例如基于事件的摄像机和（神经形态）异步硬件。我们通过对传统的螺纹机制进行基准测试，探讨了共同设置中的效果，并发现Aestream至少提供了两倍的吞吐量。然后，我们在GPU上的实时边缘检测任务中应用AEstrook，并在存储器操作少5倍的情况下，演示了1.3倍的处理。
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.
