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Found 9 papers, 0 papers with code
Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks
The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation: how to accurately account for the inherent stochastic nature of certain devices.
Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
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
Superconducting optoelectronic hardware is being explored as a path towards artificial spiking neural networks with unprecedented scales of complexity and computational ability.
Superconducting Optoelectronic Neurons II: Receiver Circuits
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
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
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.
Circuit designs for superconducting optoelectronic loop neurons
Superconducting detectors enable signaling with single photons for maximal energy efficiency.
Superconducting Optoelectronic Neurons IV: Transmitter Circuits
A superconducting optoelectronic neuron will produce a small current pulse upon reaching threshold.
Superconducting Optoelectronic Neurons V: Networks and Scaling
By modeling the physical size of superconducting optoelectronic neurons, we calculate the area of these networks.
