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Sonia Buckley (Fed)

My research at NIST has spanned from integrated and quantum photonics to neuromorphic computing and hardware for AI. I am interested in the development of next generation computing and networking technologies, which will involve heterogeneous and application-specific hardware. In particular, I am developing some of the measurement and benchmarking techniques and services needed for these technologies become commercially viable. On the quantum side, I work on the quantum radiometry project, developing a single photon detector calibration service at NIST. We are also working to develop the next generation of single photon detector measurement tools, applicable to waveguide-coupled single photon detectors on photonic integrated circuits. On the AI side, I am working on general techniques for training emergent hardware for AI. As part of this project, I am involved in a community-driven effort to benchmark next-generation neuromorphic technologies.

Awards

  • National Research Council Research Associateship Program (RAP) Award - 2015
  • Ross N. Tucker Memorial Scholarship of the Electronics Materials Symposium - 2013
  • National Science Foundation Graduate Fellowship - 2009
  • Stanford Graduate Fellowship - 2009

Publications

Photonic Online Learning

Author(s)
Sonia Buckley, Adam McCaughan, Bakhrom Oripov
Training in machine learning necessarily involves more operations than inference only, with higher precision, more memory, and added computational complexity

Patents (2018-Present)

Diagram showing the circuit for resetting the Spectral Power Distribution (SPD) after the Light Emitting Diode (LED) has emitted a photonic pulse.

Superconducting Opto-Electronic Transmitter Circuit

NIST Inventors
Sonia Buckley , Adam McCaughan and Jeff Shainline
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.
The figure illustrates a system for parameter multiplexed gradient descent in accordance with embodiments of the present invention.

System And Method For Parameter Multiplexed Gradient Descent

NIST Inventors
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.

Neuromimetic Circuit

NIST Inventors
Jeff Shainline and Sonia Buckley
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
Line drawing of the thermal impedance amplifier

Thermal Impedance Amplifier

NIST Inventors
Adam McCaughan , Varun Verma and Sonia Buckley
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
Created June 1, 2019, Updated June 20, 2023
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