计算机科学
可扩展性
任务(项目管理)
接口(物质)
动作(物理)
人机交互
人工智能
光子学
人工神经网络
计算机体系结构
并行计算
物理
管理
气泡
量子力学
数据库
最大气泡压力法
光学
经济
作者
Piotr Antonik,Nicolas Marsal,Daniel Brunner,Damien Rontani
标识
DOI:10.1038/s42256-019-0110-8
摘要
The recognition of human actions in video streams is a challenging task in computer vision, with cardinal applications in e.g. brain-computer interface and surveillance. Deep learning has shown remarkable results recently, but can be found hard to use in practice, as its training requires large datasets and special purpose, energy-consuming hardware. In this work, we propose a scalable photonic neuro-inspired architecture based on the reservoir computing paradigm, capable of recognising video-based human actions with state-of-the-art accuracy. Our experimental optical setup comprises off-the-shelf components, and implements a large parallel recurrent neural network that is easy to train and can be scaled up to hundreds of thousands of nodes. This work paves the way towards simply reconfigurable and energy-efficient photonic information processing systems for real-time video processing.
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