论文标题
针对特定发射极标识的深延迟循环储层计算
Deep Delay Loop Reservoir Computing for Specific Emitter Identification
论文作者
论文摘要
战术边缘的当前AI系统缺乏支持现场培训和对情境意识的推论的计算资源，并且由于安全性，带宽和任务延迟要求，利用回程资源并不总是很实际。 We propose a solution through Deep delay Loop Reservoir Computing (DLR), a processing architecture supporting general machine learning algorithms on compact mobile devices by leveraging delay-loop (DL) reservoir computing in combination with innovative photonic hardware exploiting the inherent speed, and spatial, temporal and wavelength-based processing diversity of signals in the optical domain.与最先进的情况相比，DLR可以减少形式，硬件复杂性，功耗和延迟。 DLR可以用单个光子DL和一些电流组件实现。在某些情况下，多个DL层增加了DLR的学习能力而不会增加延迟。我们证明了DLR在适用RF特定发射极标识的应用方面的优势。
Current AI systems at the tactical edge lack the computational resources to support in-situ training and inference for situational awareness, and it is not always practical to leverage backhaul resources due to security, bandwidth, and mission latency requirements. We propose a solution through Deep delay Loop Reservoir Computing (DLR), a processing architecture supporting general machine learning algorithms on compact mobile devices by leveraging delay-loop (DL) reservoir computing in combination with innovative photonic hardware exploiting the inherent speed, and spatial, temporal and wavelength-based processing diversity of signals in the optical domain. DLR delivers reductions in form factor, hardware complexity, power consumption and latency, compared to State-of-the-Art . DLR can be implemented with a single photonic DL and a few electro-optical components. In certain cases multiple DL layers increase learning capacity of the DLR with no added latency. We demonstrate the advantages of DLR on the application of RF Specific Emitter Identification.
