Search Results for author: Sae Woo Nam

Found 12 papers, 2 papers with code

Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation

no code implementations5 Mar 2023 Adam N. McCaughan, Bakhrom G. Oripov, Natesh Ganesh, Sae Woo Nam, Andrew Dienstfrey, Sonia M. Buckley

We present multiplexed gradient descent (MGD), a gradient descent framework designed to easily train analog or digital neural networks in hardware.

Demonstration of Superconducting Optoelectronic Single-Photon Synapses

no code implementations20 Apr 2022 Saeed Khan, Bryce A. Primavera, Jeff Chiles, Adam N. McCaughan, Sonia M. Buckley, Alexander N. Tait, Adriana Lita, John Biesecker, Anna Fox, David Olaya, Richard P. Mirin, Sae Woo Nam, Jeffrey M. Shainline

Superconducting optoelectronic hardware is being explored as a path towards artificial spiking neural networks with unprecedented scales of complexity and computational ability.

Multi-pulse fitting of Transition Edge Sensor signals from a near-infrared continuous-wave source

1 code implementation22 Aug 2018 Jianwei Lee, Lijiong Shen, Alessandro Cerè, Thomas Gerrits, Adriana E. Lita, Sae Woo Nam, Christian Kurtsiefer

Transition-edge sensors (TES) are photon-number resolving calorimetric spectrometers with near unit efficiency.

Instrumentation and Detectors Quantum Physics

Superconducting Optoelectronic Neurons II: Receiver Circuits

no code implementations7 May 2018 Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Manuel Castellanos-Beltran, Christine A. Donnelly, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam

The current from many synaptic connections is inductively coupled to a superconducting loop that implements the neuronal threshold operation.

Superconducting Optoelectronic Neurons I: General Principles

no code implementations4 May 2018 Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Jeff Chiles, Richard P. Mirin, Sae Woo Nam

The design of neural hardware is informed by the prominence of differentiated processing and information integration in cognitive systems.

Superconducting Optoelectronic Neurons III: Synaptic Plasticity

no code implementations4 May 2018 Jeffrey M. Shainline, Adam N. McCaughan, Sonia M. Buckley, Christine A. Donnelly, Manuel Castellanos-Beltran, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam

As a means of dynamically reconfiguring the synaptic weight of a superconducting optoelectronic loop neuron, a superconducting flux storage loop is inductively coupled to the synaptic current bias of the neuron.

Superconducting Optoelectronic Neurons V: Networks and Scaling

no code implementations4 May 2018 Jeffrey M. Shainline, Jeff Chiles, Sonia M. Buckley, Adam N. McCaughan, Richard P. Mirin, Sae Woo Nam

By modeling the physical size of superconducting optoelectronic neurons, we calculate the area of these networks.

Clustering

Superconducting Optoelectronic Neurons IV: Transmitter Circuits

no code implementations4 May 2018 Jeffrey M. Shainline, Adam N. McCaughan, Sonia M. Buckley, Richard P. Mirin, Sae Woo Nam

A superconducting optoelectronic neuron will produce a small current pulse upon reaching threshold.

Superconducting optoelectronic circuits for neuromorphic computing

no code implementations30 Sep 2016 Jeffrey M. Shainline, Sonia M. Buckley, Richard P. Mirin, Sae Woo Nam

To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons.

Cannot find the paper you are looking for? You can Submit a new open access paper.