$850 – $1,125

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

Event Information

Share this event

Date and Time

Location

Location

SpringHill Suites by Marriott Belmont Redwood Shores

1401 Shoreway Road

Belmont, CA 94002

View Map

Refund Policy

Refund Policy

Refunds up to 7 days before event

Eventbrite's fee is nonrefundable.

Event description

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


Share with friends

Date and Time

Location

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.

Save This Event

Event Saved