157 citations · 371 across the 9 of their papers we have counts for
4 papers · 1 filter
Is the Number of Trainable Parameters All That Actually Matters?
Amélie Chatelain, Amine Djeghri, Daniel Hesslow +2
Recent work has identified simple empirical scaling laws for language models, linking compute budget, dataset size, model size, and autoregressive modeling loss. The validity of th…
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…
Contrastive Embeddings for Neural Architectures
Daniel Hesslow, Iacopo Poli
The performance of algorithms for neural architecture search strongly depends on the parametrization of the search space. We use contrastive learning to identify networks across di…