Hardware for deep learning and mobile autonomy

Artificial Learning
Ultra-efficient integrated circuits for machine learning
Hardware for deep learning
and mobile autonomy
Business model & market strategy
www.artificiallearning.com
@ArtificialLearn
Copyright © 2014 Artificial Learning Ltd All rights reserved2014-08-06 1

OPPORTUNITY
Artificial Learning
2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 2

Sensed environment Affected environment
Learn individuals’
features
Recognise and
classify
individuals • Resident?
• Stranger?
Act on what is
recognised
Autonomous deep learning
Learn Recognise Act

Four problems we address
1. Deep learning today is (very) inefficient
• Cannot tie autonomous system to the cloud
2. Deep learning today is unscalable
• Need 2 x Google global just to learn YouTube
• Torrent from IoT sensors will swamp the cloud
3. Biology beats technology by »108 - how?
• Deep learning algorithms unsuited to standard
processors
• Moore and Dennard scaling have stopped
4. Tough route to market for disruptive tech
• Silicon economics requires mass markets
• No established autonomous deep-learning market
versus

Our solutions
Ultra-efficient integrated circuits for machine learning
• Fast, cool, low mass, low volume hybrid ASICs
• Novel highly-scalable architecture
• Inevitable: “Everything good becomes hardware.”
–Nat Torkington, O’Reilly Radar, Machine Learning on a Board review, May 2014
A roadmap to mass markets for our IP
• Agreed R&D plan
• Market development before commitment to new silicon
MLoaB
MLoaC
MLbD

Unique value propositions
• For ultra-high efficiency deep machine learning our
ground-breaking chip designs can be 10,000x more
efficient than conventional CPUs
• Unlike general purpose computers, our dedicated chip
designs can help you deploy powerful machine learning
in autonomous mobile apps
• Machine Learning on a Board lets you easily create
products able to learn, recognise and act

Unfair advantage
• Our unique technology gives us orders of magnitude advantage
in scalable efficiency

Business model
Lean start-up model
evolved rapidly through
hypothesis testing
leanlaunchlab.com
Our market strategy
made front page of
most-voted list in 2014
Stanford Technology
Entrepreneurship class
bit.ly/TPE2MostVotedList

Lessons learned
We need early adopters to
create novel mass-market
products
We gain advantage if we put
plug-compatible interfaces in
front of disruptive IP
We can grow through several
market stages and product
generations

MARKET STRATEGY
Artificial Learning

Market segments
Market
segment
Products
Outcomes
Markets
Innovators & early
adopters
Plug-compatible circuit
board and application
software
Novel applications of
autonomous machine
learning
Proof of market before
heavy investment in silicon
10’s of channel partners x
10,000s of machine-learning
enhanced products / year
AND 10,000s of enthusiasts
x 1-2 boards / year
Early majority
Packaged chips
Our chips on customers’
own circuit boards for
specific products
100s of companies buying
millions of chips / year
Mass market
Licenced IP
Our IP in customers’
systems-on-chips
100s of companies using IP
in 10s of millions of devices
/ year
Machine Learning on a Board
Machine Learning on a Chip
Machine Learning by Design

Unitvolume(logscale)
Unit price (log scale)
Personal
mobile
Makers &
Hobbyists
Industrial
IoT
Product classes which can be enhanced
by autonomous machine learning
Industrial
R&D
Academic
research
Channels for embedded ML
Target
market
Early
majority /
low volume
products
Mass marketMass market
& specialised
niches
Early
adopters
Machine
Learning
on a Chip
Machine
Learning by
Design IP
Machine
Learning
on a Board
Target
product
Defence
Space
Domestic
IoT
Commercial
autonomous
systems
Private
autonomous
systems
Initial estimates - being refined now

MLoaB generations
Ling
•Simple digital
emulation
•COTS + Open
Source
Yi
•GPU-
enhanced
emulation
•RaspberryPi
minimal
configuration
Er
•FPGA-
enhanced
emulation
•Kickstarter?
San
•ASIC package
Plug-compatible upgrades with million-fold performance increase

Market development
Present
aims
• Create demand
• Establish channel partnerships
• Increase market knowledge and visibility
• Shape minimum viable products
Present
activity
• Engagement with market influencers
• Meet with potential channel partners
• Interviews with early adopters
• Online surveys / calls for proposals

Market makers

Hardware platforms
MLaaS MLoaCMLoaB MLbD

CORPORATE INFORMATION
Artificial Learning

1980
• Co-
founders
met
2009-2010
• Frameworks
for ultra-
efficient
machine
learning
• Project
formalised
2011-2012
• Pigeon
Consortium
formed
• CMOS
feasibility
study with
Imperial
College
• Artificial
Learning Ltd
founded
2013-
2014
• Proof of
concept
plans laid
• Market
strategy
and
roadmap
Company history
Pigeon Consortium research group
• Artificial Learning Ltd
• University of Edinburgh
• Scottish Microelectronics Centre
• University of Stirling

Core team
• Artificial Learning Ltd artificiallearning.com @ArtificialLearn
Peter Newman PhD, Michael Bate MPhil
• Key concepts and IP for machine learning hardware
• Mathematical design and simulation
• Commercialisation
• Pigeon Consortium
University of Edinburgh, University of Stirling
• Our research collaboration

Partnership and allies
• Prototype R&D
– Hardware design: Pigeon Consortium
– Research funding: EPSRC
– Chip fabrication: Europractice members
• Channels – Developing these now
– End-user product manufacturers
– Maker community
• Other alliances
– Software engineers
– London Tech City network
– UK Electronic Systems Community
– Deep learning and machine learning

Risk reduction
People
Technical
Market
Financial
Research group
formed and
product
development
team skills
identified
Feasibility study
completed and
R&D plan
agreed
Current focus of
risk reduction
efforts: market
gaps, value,
channels
R&D costs
understood but
production and
sales costs to
be determined

Investment Readiness
4
9. Validate metrics that matter
8. Validate left side of canvas
7. Prototype high-fidelity MVP
6. Validate right side of canvas
5. Validate product/market fit
4. Prototype low-fidelity MVP
3. Problem-solutions validation
2. Market size/competitive analysis
1. Complete first-pass canvas
Tasks in hand to
move to IRL 5:
• Identify real-world
applications and
quantify markets
• Lean prototyping
minimum viable
product designs
tested with
potential early
adopters
• Develop channel
partnerships

Technology Readiness - MLoaC
4
9. Flight-proven in operations
8. Flight-qualified in test/demo
7. Prototype in live environment
6. Prototype in relevant environment
5. Component validation relevant env.
4. Component validation lab environment
3. Analytical/experimental proof of concept
2. Technology concept/applications formed
1. Basic principles observed and reported
Tasks in hand to
move to TRL 5:
• Obtain funding for
Pigeon Consortium
development
programme
• Device functional
simulations under
way
• Identifying IP
alliances for next
steps
• Team building

Hardware for deep learning and mobile autonomy

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