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Hardware for deep learning and mobile autonomy

The document discusses Artificial Learning, a company developing ultra-efficient integrated circuits for machine learning applications. It aims to address the inefficiencies of today's deep learning techniques by developing novel chip architectures and dedicated hardware. The company's products include Machine Learning on a Board, a plug-compatible circuit board, Machine Learning on a Chip packaged chips, and licensable Machine Learning by Design IP. Its strategy is to target early adopters, then the mass market through successive generations of products.

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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
2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 3
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
2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 4
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
2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 5
MLoaB
MLoaC
MLbD
Unique value propositions
2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 6
• 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

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Hardware for deep learning and mobile autonomy

  • 1. 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
  • 2. OPPORTUNITY Artificial Learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 2
  • 3. Sensed environment Affected environment Learn individuals’ features Recognise and classify individuals • Resident? • Stranger? Act on what is recognised Autonomous deep learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 3 Learn Recognise Act
  • 4. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 4 versus
  • 5. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 5 MLoaB MLoaC MLbD
  • 6. Unique value propositions 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 6 • 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
  • 7. Unfair advantage • Our unique technology gives us orders of magnitude advantage in scalable efficiency 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 7
  • 8. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 8
  • 9. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 9
  • 10. MARKET STRATEGY Artificial Learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 10
  • 11. Market segments 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 11 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
  • 12. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 12 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
  • 13. MLoaB generations 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 13 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
  • 14. Market development 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 14 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
  • 15. Market makers 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 15
  • 16. Hardware platforms 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 16 MLaaS MLoaCMLoaB MLbD
  • 17. CORPORATE INFORMATION Artificial Learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 17
  • 18. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 18 Pigeon Consortium research group • Artificial Learning Ltd • University of Edinburgh • Scottish Microelectronics Centre • University of Stirling
  • 19. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 19
  • 20. 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 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 20
  • 21. Risk reduction People Technical Market Financial 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 21 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
  • 22. Investment Readiness 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 22 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
  • 23. Technology Readiness - MLoaC 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 23 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