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Hardware for deep learning and mobile autonomy

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Artificial Learning Ltd Business model & market strategy www.artificiallearning.com @Artificiallearn

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Hardware for deep learning and mobile autonomy

  1. 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. 2. OPPORTUNITY Artificial Learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 2
  3. 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. 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. 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. 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. 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. 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. 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. 10. MARKET STRATEGY Artificial Learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 10
  11. 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. 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. 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. 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. 15. Market makers 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 15
  16. 16. Hardware platforms 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 16 MLaaS MLoaCMLoaB MLbD
  17. 17. CORPORATE INFORMATION Artificial Learning 2014-08-06 Copyright © 2014 Artificial Learning Ltd All rights reserved 17
  18. 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. 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. 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. 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. 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. 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

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