Building Innovative Hardware in an Era of Artificial Intelligence Building Innovative Hardware in an

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  • Building Innovative Hardware in an Era of Artificial Intelligence

    Igor Carron, CEO and Co-Founder

    LightOn.ai

  • Why Build Innovative Hardware?

    2Copyright LightOn

  • 3Source: Rebooting the IT Revolution: A Call to Action, SIA, SRC, 2015

  • 4Source: Mike P. Frank, SNL

  • 5

    It’s worse!

  • 6Source: Twitter, Coinbase

  • 7 Source: BBC

  • 8

    It’s much worse!

    Copyright LightOn

  • The Footprint of Machine Learning

    9

    CO2 eq.

  • 10Copyright LightOn

  • 11

  • LightOn Confidential and Proprietary 12

  • Scaling up a new technology from the ground up….fast

    14/06/2019 LightOn SAS 13

  • Scaling up a Technology fast

    14Source: European Commission

  • Unlike NASA/FAA, we don’t have 15+ years

    15Source: NASA

  • Surfing on Moore’s Law

    16 Source: TI, Huawei

  • Surfing Moore’s Law and Using Nature

    17Copyright LightOn

  • A Fast and Large Random Projection

    18

    Linear

    system Discrete

    input vector

    Discrete

    output vector

    (speckle intensity)

    x Random transmission matrix

    H y = | H x |2

    EXTRA-LARGE H of size higher than

    106 x 106

    (TBs of memory)

    & SUPER-FAST

    kHz operation →103 such

    multiplies / s

    Copyright LightOn

  • How it works

    19

    OPU

    Results

    Data pre- processing

    Laser Camera Optical Random

    Projections

    DAC ADC

    Data in Data out 010001…01111001010101…

    Data processing CPU / GPU

    “Big & Fat” Data

    Spatial Light Modulator

    OPU

    Copyright LightOn

  • Climbing up the TRL scale fast

    20Copyright LightOn

    TRL 3-4 (2016) TRL 5 (2017)

    TRL 6-7 (2018-2019)

    Fast prototyping among a diverse set of expertise

  • 21

    Optical Processing Unit

    Copyright LightOn

  • Why Random Projections?

    14/06/2019 22 Source: Volkan Cevher, EPFL

  • OPU: No need for large memory

    23Copyright LightOn

  • OPU: No need for large memory

    24 Copyright LightOn

  • A Machine Learning example

    14/06/2019 Copyright LightOn 25

    Speed Power consumption

    x8 -90%

    On a typical

    Machine Learning

    training task

    (transfer learning) + +

  • Other Uses Already Investigated

    26

    Image

    classification Recommender

    systems

    Graph

    analysis Anomaly

    detection

    NLP

    Video

    classification

    Copyright LightOn

  • Recommender Systems shape our lives at scale!

    Recommender Systems

    Copyright LightOn

  • Randomized Matrix Decompositions using R, Aug 2016, N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz

    Randomized Linear Algebra

    Copyright LightOn

  • 29

    LightOn.ai Copyright LightOn