We develop photonic hardware called Optical Processing Units – or OPUs – of interest to Artificial Intelligence.
The technology boosts some generic tasks in Machine Learning such as training and inference of high-dimensional data. It can be used in the context of supervised and unsupervised learning, with batch processing or streaming data.
OPUs are highly integrated with CPUs and GPUs so that it boosts their respective performance.
OPUs can be seamlessly accessed through an Open Source Python API called LightOnML and through a lower level, proprietary API called LightOnOPU.
Benchmarks to compare the performance of CPU and GPU with our OPU are available here: https://github.com/lightonai/opu-benchmarks
The brain of the current OPUs is Nitro, the first generation Photonic Core – Nitro drives the Aurora 1.5 available in LightOn Cloud.
The Nitro photonic core implements specific matrix-vector multiplications in a massively parallel fashion, where inputs and outputs of dimension up to one million are processed at once, within a single clock cycle.
More precisely, a Nitro photonic core performs Random Projections at the equivalent of 2.1012 hybrid-precision MAC (multiply-accumulate) operations every clock cycle.
The Nitro photonic core thus yields a stunning performance of 3 PetaOP/S with only 30 Watts of power or about 100 TeraOP/J.
MD simulations are used in drug design, new materials discovery
Detect in real-time changes, without the need to store the whole history of the graph
Enables OPUs to become a cornerstone in the training process-optical training.
Library: no dedicated library yet
Dataset: widely applicable (RecSys, graphs, NLP, etc.)
ArXiv
GitHub
Dimensionality reduction makes the whole processing pipeline faster.
Using information from the labels produces more useful embeddings.
The Nitro photonic core leverages light scattering to perform a specific kind of matrix-vector operation called Random Projections.
Random Projections have a long history for the analysis of large-size data since they achieve universal data compression. In other words, you can use this tool to reduce the size of any type of data, while keeping all the important information that is needed for Machine Learning. There are well-established mathematical guarantees on why this works, in particular thanks to a result known as the Johnson-Lindenstrauss lemma.
Essentially, this states that compression through Random Projections approximately preserves distances: if two items are similar in the original domain, their compressed version will also be similar.
Going from mathematics to various fields of engineering, Random Projections have proved useful in a wide variety of application domains such as Compressed Sensing, Randomized Numerical Linear Algebra, Hashing, Streaming and Sketching algorithms. More recently, they have naturally found their way into Machine Learning, for instance in Deep Neural Networks, Reinforcement Learning or Reservoir Computing.
It’s important to stress that such Random Projections can be applied to any type of data: images or videos, sound, financial or scientific data or more abstract features … anywhere where one is “drowned in data but starved for wisdom”.
Besides developing the hardware, LightOn AI Research team investigates numerous use-cases for our unique digital-analog hybrid technology. We communicate to the science and tech community through journal papers, preprints, conference presentations, blog posts, workshops and meetups. We also have detailed examples in our API documentation pages and GitHub account. Have you done anything mind-blowing on LightOn Cloud? Let us know and we’d love to talk about it!