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Found 4 papers, 0 papers with code
Scaled-Time-Attention Robust Edge Network
We demonstrate that STARE is applicable to a variety of applications with improved performance and lower implementation complexity.
Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors
We propose a solution via Deep Delay Loop Reservoir Computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electrooptical hardware.
Reservoir-Based Distributed Machine Learning for Edge Operation
We introduce a novel design for in-situ training of machine learning algorithms built into smart sensors, and illustrate distributed training scenarios using radio frequency (RF) spectrum sensors.
Deep Delay Loop Reservoir Computing for Specific Emitter Identification
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.
