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https://www.nist.gov/patents/inventors/1172691
Search Patents by Sonia Buckley
Patents listed here reflect only technologies patented from FY 2018-present. To view all of NIST's patented technologies, visit the NIST pages on the Federal Laboratory Consortium website.
Adam McCaughan
,
Sonia Buckley
and
Andrew Dienstfrey
Parameter multiplexed gradient descent (PMGD) is a machine learning training method designed to easily train emergent non-volatile memory and neuromorphic hardware platforms.
This invention describes a family of circuits that receive an input current or voltage pulse when a superconducting element has been driven above threshold, producing an amplified current or voltage pulse to drive a light source, and reset to the resting state after light has been generated.
A thermal impedance amplifier includes: a resistive layer including: a resistance member; a first electrode in electrical communication with the resistance member; and a second electrode in electrical communication with the resistance member; a switch layer opposing the resistive layer and including
Optoelectronic neural networks comprise a system of interconnected processing units (neurons) interconnected by integrated photonic waveguides. The processing units receive photonic signals from other units. Each unit sums the received signals on a waveguide-integrated photon detector, and when the