计算机科学
异步通信
管道(软件)
延迟(音频)
嵌入式系统
水准点(测量)
低延迟(资本市场)
卷积神经网络
边缘计算
边缘设备
实时计算
无线传感器网络
智能摄像头
GSM演进的增强数据速率
计算机硬件
计算机网络
人工智能
操作系统
云计算
电信
大地测量学
地理
作者
Ole Richter,Yannan Xing,Michele De Marchi,Carsten M. Nielsen,Merkourios Katsimpris,Roberto Cattaneo,Yudi Ren,Qian Liu,Sadique Sheik,Tugba Demirci,Ning Qiao
出处
期刊:Cornell University - arXiv
日期:2023-04-13
标识
DOI:10.48550/arxiv.2304.06793
摘要
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.
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