24 citations · 69 across the 11 of their papers we have counts for
12 papers
Measurement-Adapted Eigentask Representations for Photon-Limited Optical Readout
Tianyang Chen, Mandar M. Sohoni, Saeed A. Khan +5
Optical readout in low-light imaging is fundamentally limited by measurement noise, including photon shot noise, detector noise, and quantization error. In this regime, downstream…
A fully parallel densely connected probabilistic Ising machine with inertia for real-time applications
Ruomin Zhu, Abhishek Kumar Singh, Jérémie Laydevant +5
Ising machines---special-purpose hardware for heuristically solving Ising optimization problems---based on probabilistic bits (p-bits) have been established as a promising alternat…
Ultra-low-light computer vision using trained photon correlations
Mandar M. Sohoni, Jérémie Laydevant, Mathieu Ouellet +7
Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage o…
Machine vision with small numbers of detected photons per inference
Shi-Yuan Ma, Jérémie Laydevant, Mandar M. Sohoni +3
Machine vision, including object recognition and image reconstruction, is a central technology in many consumer devices and scientific instruments. The design of machine-vision sys…
Roadmap on Neuromorphic Photonics
Daniel Brunner, Bhavin J. Shastri, Mohammed A. Al Qadasi +147
This roadmap consolidates recent advances while exploring emerging applications, reflecting the remarkable diversity of hardware platforms, neuromorphic concepts, and implementatio…
Self-Contrastive Forward-Forward Algorithm
Xing Chen, Dongshu Liu, Jeremie Laydevant +1
Agents that operate autonomously benefit from lifelong learning capabilities. However, compatible training algorithms must comply with the decentralized nature of these systems, wh…
Unsupervised End-to-End Training with a Self-Defined Target
Dongshu Liu, Jérémie Laydevant, Adrien Pontlevy +2
Designing algorithms for versatile AI hardware that can learn on the edge using both labeled and unlabeled data is challenging. Deep end-to-end training methods incorporating phase…
The hardware is the software
Jeremie Laydevant, Logan G. Wright, Tianyu Wang +1
Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the microscopic physics of artificial-intelligence hardware and of h…
Quantum-limited stochastic optical neural networks operating at a few quanta per activation
Shi-Yuan Ma, Tianyu Wang, Jérémie Laydevant +2
Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved…
Training an Ising Machine with Equilibrium Propagation
Jérémie Laydevant, Danijela Markovic, Julie Grollier
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins…
Training Dynamical Binary Neural Networks with Equilibrium Propagation
Jérémie Laydevant, Maxence Ernoult, Damien Querlioz +1
Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical networks, thanks to the local updates of weights given by the internal dynamics of th…
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
Erwann Martin, Maxence Ernoult, Jérémie Laydevant +4
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge.…