$850 – $1,125

Real-Time Low-Power AI at the Edge: 2.5-Day Raspberry Pi Training Event

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SpringHill Suites by Marriott Belmont Redwood Shores

1401 Shoreway Road

Belmont, CA 94002

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Event description


Real-Time Low-Power AI at the Edge: 2.5 Day Training Event for the Development of Mission-Critical Applications

RTML Neuromem Chip will be delivered at the training on a RasberryPi enabled board.

Training Program

Day 1 - Welcome

Real-Time Machine Learning closer to life:

  • Mission statement: The need for pervasive generic vision

  • Conceived the hardware architecture of NeuroMem

  • Expert in using NeuroMem for real-time embedded artificial vision

  • Merging different sensors (sound, seismic, chemical, etc) with vision sensors

  • Artificial perception is “a must” for useful Artificial Intelligence.

NeuroMem is alive and ready to use!

  • Fielded applications

  • Demonstrations

    • Embedded Vision

    • Embedded Signal

    • Instant access to parallel memory

Concept introduction - An alternative way

  • Pattern recognition is supporting all biological activities

    • Reproduction

    • Survival

    • Behavior

    • Education

    • Mobility

    • Motricity

  • Inspired by the neo-cortex

  • Intelligence is about “memory”

  • Memory access should be parallel

  • Unrestricted Trainable Fuzzy Look Up Memory as a pattern recognition future?

  • Matching output with inputs

Historical review

  • Neural network an old concept?

    • Early days

    • Resurgence in 1988

    • Back in the dark age

    • IBM ZISC a venture with IBM and G. Paillet in 1993

      • Derived from Batchelor and Leon N. Cooper models

        • Compound classifier (Bruce Batchelor 1969)

        • RCE (restricted Coulomb Energy (Cooper, Elbaum ,Reilly 1972)

      • Innovative architecture inspired by massive parallel architecture (CERN – Geneva -1984)

    • Semiconductor technology finally allows it (2005- )

Limitation of current technologies including deep learning

  • Does not scale easily

  • No incremental training capability

  • Speed intrinsically decreases with the complexity of problems

  • Needs thousands of examples if not more for a simple task.

  • Multiples models lead to decreasing speed

  • High power

  • Medium to a large footprint

Bioinspired pattern recognition hardware complementary to Von Neumann model

  • No software

  • Non-linear classification

  • Unlimited scalability

  • Constant response time

  • Miniature

  • Ultra-low-power

  • Unspecialized user trainable

Understanding the need for non-linear mapping

  • What is a pattern

    • The pattern as a raw data or derived signature

    • Mapping a highly dimensional parameters space

    • Comparison of useful metrics

  • Features (parameters) space mapping

    • Peculiarities in features spaces

    • How to resolve them

  • Phenomenon affecting pattern stability

    • In vision

    • In signal

    • In dataset

NeuroMem is an automatic model generator

  • Learning mechanism

  • Dealing with uncertainty

Building a training and validation dataset

  • Must be representative of the population to recognize

  • Should be as large as practicable in order to increase detection probabilities (speed is not an issue for NeuroMem hardware)

  • If no real data available computed models will work

  • Annotating the data set

    • What is annotation

    • Some annotation needed for training

    • More annotated data even needed for thorough validation

    • Cross annotation if possible

    • Using NeuroMem for detecting annotation inconsistencies

Day 2 - Beyond the silicon chip, the Intelligence…

Intelligence, can we understand it?

  • Sensory stimulation

  • Experience for building knowledge

  • Developing inhibition (reward/penalty system)

What is an expert?

  • Start with textbook knowledge (supervised training)

  • Find unusual case, develop a unique expertise

  • The “credit assignment” (reward/penalty)

  • Expertise cloning

Rules-based systems

  • Rules versus “intuition”

  • Not everything can be formalized (limit of understanding)

  • When a rule is accurate, use it!

  • Traffic light analogy

Expert domains

  • Designing experts with domain in mind

  • Training of a single expert by example

  • Type of expertise

    • Shape

    • Colors

    • Others

    • Multi-sensors experts

Unary decision-making process

  • Throughput versus accuracy

  • Cost of error

  • Increase throughput or/and accuracy

  • Unary uncertainty management

  • Adjusting conservatism versus error cost function

Collective decision-making process

  • Organizing multi-expert panel

  • Rating individual expert knowledge

  • Defining expert hierarchy versus skills

  • Panel management

  • Parallel experts

  • Sequential expert

  • Dealing with uncertainty

  • Reaching unanimity or consensus

Day 3 - Workshop

General Vision Tools

Image Knowledge Builder

NeuroMem Learning System

General Vision supplied applications

  • Vision

    • Live

    • In storage

  • Signals

    • Audio

    • Bio

Users provided applications with GV staff assistance

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Date and Time


SpringHill Suites by Marriott Belmont Redwood Shores

1401 Shoreway Road

Belmont, CA 94002

View Map

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

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