no code implementations • 9 Jul 2021 • Richard Lau, Lihan Yao, Todd Huster, William Johnson, Stephen Arleth, Justin Wong, Devin Ridge, Michael Fletcher, William C. Headley
We demonstrate that STARE is applicable to a variety of applications with improved performance and lower implementation complexity.
no code implementations • 1 Apr 2021 • Silvija Kokalj-Filipovic, Paul Toliver, William Johnson, Rob Miller
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.
no code implementations • 1 Apr 2021 • Silvija Kokalj-Filipovic, Paul Toliver, William Johnson, Rob Miller
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.
no code implementations • 13 Oct 2020 • Silvija Kokalj-Filipovic, Paul Toliver, William Johnson, Raymond R. Hoare II, Joseph J. Jezak
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.