Photonic computing for Machine Learning at scale
LightOn OPU: the Photonic AI Chip to unlock transformative AI
We develop photonic computing hardware (Optical Processing Units – OPUs) of interest to Artificial Intelligence (AI) and HPC.
LightOn’s photonic computing 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, available here: https://github.com/lightonai/lightonml
Benchmark code to compare the performance of CPU and GPU with our OPU are available on Github

The OPU is the first large-scale hybrid digital / analog computing platform for AI.

On-premises
LightOn Appliance is our offer for on-premises OPU technology. The Appliance is the most advanced photonic AI/HPC co-processor on the market today reaching a maximum capacity of 1500 TeraOPS at 30 W TDP.
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Cloud Computing
Discover the potential of LightOn’s OPU technology through our LightOn Cloud platform. Get an account today and see how simple it is to perform Machine Learning tasks using our Python SDK.
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Photonic Quantum Computing
LightOn Qore: A novel Quantum Photonic Processor
LightOn Qore quantum photonic processors are all versatile, powerful, and low-loss platforms designed for the rapidly growing field of NISQ (Noisy Intermediate Scale Quantum) computing. Available: Q2 2022.
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Use cases
Machine Learning Techniques

Neural Network Training: Direct Feedback Alignment

Why is the training of Neural Networks with an OPU important?

 

  • Enables OPUs to become a cornerstone in the training process-optical training.

     

  • Great potential for faster training/larger models.

Demonstrated on a large range of tasks — Optical training demonstrated on fully connected networks, graph convolution networks, and GPTs

Background:

  • Background work: Direct Feedback Alignment https://arxiv.org/abs/1609.01596

  • Library: no dedicated library yet

  • Dataset: widely applicable (RecSys, graphs, NLP, etc.)

  • Next-generation photonic core Lazuli 1.5 (Available on LightOn Cloud by Q3 2021)

 

Deeper Insight

NeurIPS

ArXiv

GitHub

Supervised Random Projections

Why is the fast class-aware low-D embedding of data
important?

  • Dimensionality reduction makes the whole processing pipeline faster.

  • Using information from the labels produces more useful embeddings.

  • It is a scalable method.

1.2 to 4x speedup at label dimensionality 100k to 700k

Background:

  • Supervised Random Projections from https://arxiv.org/abs/1811.03166
  • Library: LightOnML
  • Dataset: dimensionality reduction benchmark datasets
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Deeper Insight

Blog

Github

Computer Vision

Transfer Learning on Images

Why is Fast training of image classification models important?

 

  • The Data Science team spends less time training image classification models.
  • Allows the use of lower precision arithmetics for training/inference to further reduce training/test time.
  • Re-training SOTA models with little data are essential for businesses.
  • For data scientists: More iterations are possible on higher-level tasks.

Up to 10x faster than backprop on GPU!

Background:

  • Transfer Learning applied on 2D CNNs such as VGG, Densenet, Resnet models for image classification
  • Library: LightOnML
  • Dataset: STL10, Skin Cancer, Flowers and other image datasets
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Deeper Insight

Blog

Documentation

Transfer Learning on the OPU

Github

Transfer Learning on Videos

Why is Fast training of Video classification models important?

 

  • Training 3D CNNs is extremely time and energy-consuming.
  • Can get around the huge memory requirements of CNNs.
  • For Data scientists: More iterations are possible on higher-level tasks.
  • Much lower variance to the hyperparameters’ choice.

Training time: Up to 9x faster than backprop on GPU at the same level of accuracy!

Background:

  • Transfer learning on 3D CNNs such as I3D for video action recognition
  • Library: LightOnML
  • Dataset: HMDB51, UCF101 and other action recognition video datasets
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Approximating Kernel Ridge Regression

Why is Fast training of simple models important?

  • State-of-the-art performance with theoretical guarantees in some tasks.
  • For Data scientists: More iterations are possible on higher-level tasks.

1.3x to 23.6x faster extrapolating the curves to 1.000.000

Background:

  • Kernel ridge regression approximation for classification tasks.
  • Library: LightOnML
  • Dataset: qm7 (quantum chemistry), high energy physics data and others (image classification)
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Deeper Insight

Blog

ArXiv

Github

Natural Language Processing

NLP on survey answers

Why is Fast NLP important?

 

  • The Data Science team spends less time building NLP models and get results.
  • Re-training SOTA models with little data are essential for businesses.
  • For data scientists: more iterations are possible on higher-level tasks.

Analysis of the French National Grand Débat data

Background:

  • NLP applied on Bag Of Random Embedding Projections (BOREP)
  • Library: LightOnML
  • Dataset: Grand Débat dataset
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Deeper Insight

Blog

Ranking & Recommendations

Randomized SVD for Recommender Systems

Why is important?

 

  • To offer a quick and easy baseline for large scale recommender systems.

MovieLens 20M database: 27000 movies x 138000 users, with 0.5%non-zero entries

 

Background:

 

Deeper Insight

Docs

Time Series

Change Detection in Molecular Dynamics

Why is Change detection in Molecular Dynamics (MD) simulation important?

 

  • MD simulations are used in drug design and new materials discovery

     

  • Applying an intelligent layer on top of HPC simulation enables metadynamics
  • 15x faster than FastFood on CPU at 50k atoms!
  • OPU enables analysis of samples containing a very large number of atoms due to overcoming the memory bottleneck of traditional architectures.
  • For 700k + atoms, NEWMA RP on OPU is expected to be 30x faster than NEWMA FF on CPU!

Background:

  • No-prior-knowledge EWMA: https://arxiv.org/abs/1805.08061
  • Library: LightOnML
  • Dataset: Molecular Dynamics simulations (HPC, Anton)
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Deeper Insight

Blog

ArXiv

Github

Change Detection in Time Evolving Graphs

Why is important?

 

  • Detect in real-time changes, without the need to store the whole history of the graph

  • Applications in community detection, fraud detection, biology, and others

Reduced memory requirements and facilitating the analysis of Very large datasets

Background:

  • No-prior-knowledge EWMA: https://arxiv.org/abs/1805.08061
  • Library: LightOnML
  • Dataset: Facebook graph datasets, any time-evolving graph
  • Nitro photonic core, Aurora 1.5 (LightOn Cloud)

 

Deeper Insight

GitHub

Optical Reservoir Computing

Why is Reservoir Computing important?

 

  • It needs only the training of a linear layer compared to Recurrent Neural Networks with similar prediction capabilities on chaotic time-series
  • It is energy efficient and can be deployed on the edge
  • Using the OPU allows for larger reservoir sizes at no cost in computation or memory

Prediction capabilities on chaotic time-series

Prediction capabilities on chaotic time-series

Background:

 

Machine Intelligence

Reinforcement Learning: Model Free Episodic Control

Why is important?

 

  • It can tackle high-dimensional control problems in robotics or trading with a more efficient “memory” for exploitation.
  • Accelerate early stages of learning of an agent with imitation learning.

Background:

 

Deeper Insight

Blog

Github

A Deeper Insight

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, the 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!