Deep Delay Loop Reservoir Computing for Specific Emitter Identification
This addresses the challenge of deploying machine learning on compact mobile devices in resource-constrained environments like tactical edges, though it appears incremental as it builds on existing delay-loop reservoir computing with photonic hardware enhancements.
The paper tackles the problem of enabling AI systems at the tactical edge to perform in-situ training and inference for situational awareness by proposing Deep Delay Loop Reservoir Computing (DLR), which reduces form factor, hardware complexity, power consumption, and latency compared to state-of-the-art methods.
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.