LightOn’s technology uses light to perform some computations of interest to Machine Learning / Artificial Intelligence. Our analog computation devices literally harvest natural physical processes at unprecedented speed, size, and power efficiency.

Our first product is a hardware co-processor called the Optical Processing Unit – or OPU. It is designed to boost some of the most compute-intensive tasks in Machine Learning. The OPU can just be plugged onto a standard server or workstation, and accessed through a simple toolbox that is seamlessly integrated within familiar programming environments. Full-scale OPU prototypes are already available to selected users through the LightOn Cloud. We are opening our registration for those researchers and data scientists interested in trying out our technology on our cloud. The sign-up page is here.

How does it work ?

Matrix-vector multiplications are amongst the most important elementary computing blocks in Machine Learning. For instance, Deep Learning schemes essentially stack such matrix-vector multiplications with non-linearities. 

An OPU just does that: it multiplies the input data by a fixed matrix, passes through an element-wise non-linearity, and outputs the result. But because the OPU harnesses optics it can do this operation

  • at massive data size
  • very fast
  • at minimal power consumption

What makes each OPU device literally unique is the fixed random matrix at the core if its computations, well fitted to the statistical learning of many Machine Learning / Artificial Intelligence schemes. More technical information on how our Optical Processing Unit perform its operation can be found here.

Use cases

Examples of successful use cases of the OPU technology include:

  • Image and Video Classification
  • Recommender Systems
  • Anomaly Detection
  • Natural Language Processing
A deeper insight

Our current Optical Processing Unit performs Random Projections. These Random projections have a long history in terms of permitting the analysis of large sized data. They allow for oblivious dimensionality reduction thanks to the Johnson-Lindenstrauss lemma and have been found useful in large variety of fields such as Compressed Sensing, Randomized Numerical Linear Algebra, Streaming and Sketching algorithms…. Findings using our technology are communicated to the science community through preprints, presentations at conferences, blog posts , workshops and meetups, examples through the use of our API on LightOn Cloud, and publications.

Supervised Learning (Random Features kernels)

 

Unsupervised Learning (Randomized Numerical Linear Algebra)

 

Natural Language Processing

 

Time series analysis

 

Other: Context and General framework linking machine learning and physics (including quantum computing)