Brainchip Holdings Ltd. · ASX:BRN
Brainchip — Investor Presentation (March 2018)
21 slides from Brainchip's investor presentation, Apr 3, 2018
Slide 1: BrainChip March 2018 Investor Presentation
For personal use only Louis DiNardo President and CEO Presented March 2018 ASX CODE: BRN | “AN AI PROCESSOR COMPANY” BrainChip M A RCH 2018 | 1 MARCH 2018
Slide 2: Disclaimer
Disclaimer For personal use only This presentation is not a prospectus nor an offer for securities in any jurisdiction nor a securities recommendation. The information in this presentation is an overview and does not contain all information necessary for investment decisions. In making investment de
Slide 3: BrainChip Year-End Update
BrainChip Year-End Update For personal use only Recent Announcements Sales and Marketing Update Annual General Meeting Spiking Neural Network Technology BrainChip M A RCH 2018 | 3
Slide 4: Recent Announcements
Recent Announcements For personal use only February Establishment of • BrainChip retains 20-year experience surveillance and video analytics veteran in Europe, Mr. Luis Coello. 2018 EMEA Sales • Addition will assist in strengthening BrainChip’s market position with law enforcement and intelligence a
Slide 5: Year-end Update
BrainChip Year-end Update For personal use only • BrainChip Accelerator Card • ADAS and Autonomous Vehicle Collaboration • Europe • Detroit • Third Parties • Civil Surveillance/Public Safety • Law Enforcement • Schools • Homeland Security • Commercial Surveillance/Consumer Behavior • Gaming BrainChi
Slide 6: BrainChip's SNN Outperforms Leading Deep Learning Models
BrainChip’s SNN Outperforms Leading Deep Learning Models For personal use only 45 40 Effective Frames/Sec/Watt (Standard Definition) 35 30 25 20 15 10 5 0 Nvidia Jetson TX2 Nvidia Jetson TX2 AlexNet* BrainChip Accelerator** GoogleNet* *NVIDIA Jetson TX2 in MQ mode, NVIDIA website, 3/7/2017, adjusted
Slide 7: Year-End Update: Placements, Cash and Burn
BrainChip Year-End Update For personal use only • Successful Placements in 2017 A$27.5 million. • US$20.9M • Ended the Year with Roughly US$16M • Net Burn in December Quarter US$1.9M • Burn US$700K/month or US$2.1M/quarter provides 21 to 22 months • Akida Capital Expense on the order of US$3.0M for
Slide 8: Year-End Update: Sales Growth and Customers
BrainChip Year-End Update For personal use only • Company expects continued sales growth with original equipment manufacturers, integrators and other partners. • SaaS • Surveillance System Integrators • Milestone • Gaming Partners International • Quantum Corporation • Existing Customers • France Hom
Slide 9: Year-End Update: Sales Pipeline
BrainChip Year-End Update For personal use only • Company expects continued sales growth with original equipment manufacturers, integrators and other partners. • Sales Pipeline • 323 Leads since introduction of BCS and Accelerator • Dozens of Software Download • 100 Opportunities • 52 Qualified Oppo
Slide 10: Year-End Update: Trade Shows and Advertising
BrainChip Year-End Update For personal use only • Trade Shows • Security and Counter Terrorism Expo 2018 – March 3‐6, London, UK • Added 50 Leads, including key UK organizations • ISC West 2018 – April 10‐13 Las Vegas, USA • G2E – May 15 ‐17 Macau • Advertising • Online and Print ads across NA and E
Slide 11: Year-End Update: Akida Development Focus
BrainChip Year-End Update For personal use only • Company is focused on the development of its revolutionary AKIDA Neuromorphic Processor Unit. • ADAS and AV • FinTech • Cybersecurity • IoT BrainChip M A RCH 2018 | 11
Slide 12: AKIDA Neuromorphic (NSoC)
BrainChip AKIDATM Neuromorphic (NSoC) For personal use only Product Description • General Purpose Processor based upon Patented (2008) AKIDATM SAM: Spiking Neural Network • Fully parallel architecture $4.8 Billion* Training Method • The Neuromorphic Chip Market is estimated to be worth $4.8 Bill
Slide 13: Artificial Neural Network Competitive Landscape
Artificial Neural Network Competitive Landscape For personal use only Spiking Neural Networks Convolutional Neural Networks / Deep Learning Software Simulation of ANNs CPU CPU Cloud Acceleration Edge Acceleration Off‐the‐Shelf Hardware Acceleration Customized Acceleration Google TPU TrueNorth Loihi
Slide 14: 2018
BrainChip 2018 For personal use only • Well Capitalized • Well Staffed • Strong Intellectual Property • Commercially Available Products for Surveillance • OEM and End‐User Sales • Large Sales Pipeline • Revenue Growth • Profitability • AkidaTM Development BrainChip M A RCH 2018 | 14
Slide 15: Organizational Development
BrainChip Organizational Development For personal use only Louis DiNardo CEO Peter van der Anil Mankar Robert Beachler Ryan Benton Investor Made CDO SVP Marketing CFO Relations CTO General Aliso Viejo Aliso Viejo Europe M&C Accounting Toulouse Toulouse North America Tax Australia Treasury Public Rel
Slide 16: Spiking Neural Networks Technology
For personal use only Spiking Neural Networks Technology BrainChip M A RCH 2018 | 16
Slide 17: Traditional CPU Architecture Inefficient for ANNs
Traditional CPU Architecture Inefficient for ANNs For personal use only Traditional Compute Architecture Artificial Neural Network Architecture Memory Control Arithmetic input unit logic unit output PROCESSOR ACCUMULATOR Optimal for sequential execution Distributed, parallel, feed‐forward or back‐pr
Slide 18: Machine Learning Taxonomy
Machine Learning Taxonomy For personal use only Class of computing where Machine Learning functionality is NOT (Artificial Intelligence) explicitly programmed Artificial Neural Support Vector Bayesian Algorithmic approaches Networks Machines Networks (ANNs) Convolutional Neural Spiking Neural Networ
Slide 19: John von Neumann quote
For personal use only “We require exquisite numerical precision over many logical steps to achieve what brains accomplish in very few short steps.” 1 John von Neumann Inventor of the digital computer 1 Von Neumann J (1958) The Computer and the Brain. New Haven CT: Yale UP. BrainChip M A RCH 2018 | 1
Slide 20: Artificial Neural Network Types for Primary Compute
Artificial Neural Network Types for Primary Compute For personal use only Spiking Neural Networks Convolutional Neural Networks Low Power Math intensive Ideal for Edge / Data Acceleration Ideal for Cloud Compute ∫ ∫ ∫ Synapses Reinforced connections Linear Algebra Matrix Multiplication ∫ Neurons Inh
Slide 21: Thank You
For personal use only Thank You BrainChip M A RCH 2018 | 21
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