10 citations · 10 across the 2 of their papers we have counts for
4 papers
Streamlined optical training of large-scale modern deep learning architectures with direct feedback alignment
Ziao Wang, Kilian Müller, Matthew Filipovich +12
Modern deep learning relies nearly exclusively on dedicated electronic hardware accelerators. Photonic approaches, with low consumption and high operation speed, are increasingly c…
LightOn Optical Processing Unit: Scaling-up AI and HPC with a Non von Neumann co-processor
Charles Brossollet, Alessandro Cappelli, Igor Carron +14
We introduce LightOn's Optical Processing Unit (OPU), the first photonic AI accelerator chip available on the market for at-scale Non von Neumann computations, reaching 1500 TeraOP…
Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra
Daniel Hesslow, Alessandro Cappelli, Igor Carron +6
Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC). RandNLA provides approximate solutions to linear alge…
Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment
Julien Launay, Iacopo Poli, Kilian Müller +5
The scaling hypothesis motivates the expansion of models past trillions of parameters as a path towards better performance. Recent significant developments, such as GPT-3, have bee…