Edge computing solutions that enable the extraction of high level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (Soc), featuring an event-based camera and a low power asynchronous spiking Convolutional Neuronal Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. Moreover, the simple end-to-end nature of the SoC facilitates small stand-alone applications as well as functioning as an edge node in a larger systems. The event-driven nature of the vision sensor delivers high-speed signals in a sparse data stream. This is reflected in the processing pipeline, focuses on optimising highly sparse computation and minimising latency for 9 sCNN layers to $3.36\mu s$. Overall, this results in an extremely low-latency visual processing pipeline deployed on a small form factor with a low energy budget and sensor cost. We present the asynchronous architecture, the individual blocks, the sCNN processing principle and benchmark against other sCNN capable processors.

翻译：边缘计算解决方案能够从多种传感器中提取高层次信息，其需求日益增长。这是由于边缘端应用对传感处理有需求的智能设备数量不断增加。为解决此问题，我们提出了一种智能视觉传感器片上系统（SoC），其集成了基于事件的相机和低功耗异步脉冲卷积神经网络（sCNN）计算架构，并封装于单一芯片上。通过将传感器和处理单元集成于同一裸片，我们能够显著降低单位生产成本。此外，该SoC的端到端简洁特性既支持小型独立应用，也可作为大型系统中的边缘节点运行。视觉传感器的事件驱动特性通过稀疏数据流传递高速信号。这一特性体现在处理流水线中，其专注于优化高稀疏计算，并将9层sCNN的延迟最小化至$3.36\mu s$。总体而言，这实现了在小型化封装中部署极低延迟的视觉处理流水线，且能耗预算和传感器成本较低。我们介绍了异步架构、各功能模块、sCNN处理原理，并与其他支持sCNN的处理器进行了基准对比。