How to Start an Embedded Vision Company - Cognite · PDF fileHow to Start an Embedded Vision Company . ... Copyright © 2017 Cognite Ventures LLC 10 ... Convertible Note Private Equity

Copyright © 2017 Cognite Ventures LLC 1

Chris Rowen, PhD, FIEEE May 2017

How to Start an Embedded Vision Company


New Age: cheap pixelsè computer cognition

•  CMOS sensors trigger imaging explosion •  99% of of captured raw data is pixels (dwarfs

sounds and motion)

1010 sensors x 108 pixels/sec = 1018 raw pixels/sec

•  Rapid growth of vision products and services •  Starting 2015: more image sensors than people

0

2E+09

4E+09

6E+09

8E+09

1E+10

1.2E+10

1.4E+10

1.6E+10

1.8E+10

2E+10

1990 1995 2000 2005 2010 2015 2020

World Population

Sensor population (3 yr life)


Outline

Startups 101

Old School Startup

Lean Startup

Vision Startups

Cognitive Startups

Observations and Caveats


Startup 101 Key Ingredient: Team

•  Depth of skills – world-class in one or two essential disciplines

•  Diversity of skills – hardware, software, marketing, sales, fund-raising, strategy, infrastructure

•  Experience – enough to avoid the rookie mistakes and to recognize a pot-hole vs. a existential crevasse

•  Character – Openness, patience, communication, uncompromising honesty and commitment are crucial


Startup 101 Key Ingredient: Product Uniqueness in at least one major dimension

•  Performance (or cost, power, form-factor)

•  Functionality - previously undoable task, or combination of tasks

•  Business or usage model – often enabling non-user to become user

Feasibility •  Can you actually make the technology work in the target platform under real-world

conditions?

Defensibility •  Is it hard enough? Can you learn from customer experience faster? Can you gather data

first and best? Can you get and hold users?

Competition:

•  If you really believe you have no competition, then you have no market – find a competitor!!


Startup 101: Key Ingredient: Target Market The Grand Paradox: •  Crossing the chasm (Geoffrey Moore)

•  Not hard to find a few technology enthusiasts and early adopters who buy BECAUSE it is new/cool

•  Hard to convince mainstream customers who just want to get a job done at low risk

•  Startups often stall at this chasm

•  Approach: Narrow focus to completely solve the problem of a niche of mainstream customers

•  On the other hand: investors want huge markets, not niches

So… find huge potential markets where addressing a niche gives insights and technologies that help on the eventual breakout

Source:bostonvcblog.typepad.com


Startup 101: Develop your network

•  The magic of high-tech entrepreneurship, especially in the Bay Area, is all the free help: •  Law firms •  VCs •  Bankers •  Incubators and hosted offices •  Commercial and academic conferences, workshops and tutorials •  Huge body of experienced entrepreneurs, happy to offer free advice


Lean Startup Principles

1.  Rapidly develop “Minimum Viable Product” (MVP) 2.  Test prototypes on target users early and often 3.  Don’t take too much money too soon 4.  Measure market and technical progress

dispassionately, and scientifically 5.  Leverage open source and crowd source thinking


Old School vs. Lean Startup Models

Old School Lean Startup

Funding Seed Round based on team and idea, A Round to develop product, B Round after revenue

Develop prototype to get Seed Round, A Round after revenue, B Round, if any, for global expansion

Product Types Hardware/software systems and silicon

Easiest with software

Customer Acquisition Develop sales and marketing organization, to sell direct or build channel

CEO and CTO are chief sales people until product and revenue potential proven in the market

Business models Mostly B2B with large transactions Web–centric B2B and B2C with subscriptions and micro-transactions


Embedded vision startup guide

1.  Many leverage points on vision performance: ISP, data selection, training sets, recognizers, UI, user expectations

2.  Neural network methods are powerful tools in both discrimination (recognition, localization) and generation tasks

3.  To find, create and repurpose data is half the battle in deep learning •  Continuous data bootstrap from use •  Visual simulators for model training •  Use big models to label data for smaller, specialized embedded models

4.  New device types make new vision problems: •  AR/VR headsets •  “Visual Dust” •  Vehicles and drones

Example: AIMotive Self-driving car software •  Sensor fusion from radar,

LiDAR, cameras •  Real-time segmentation

and localization with NN •  Driving simulator to

generate training data for rare/difficult situations


Embedded vision startup guide

5.  Remarkable hardware platforms coming on the scene: • Mobile apps processors, GPUs, FPGAs, CNN engines • Choose your target cost & power. Fit algorithms to platform

6.  Real applications rarely 100% vision: • Combine with other channels – radar, lidar, audio, MEMS • Aggregate for essential control functions: robotics, safety,

security alerts, correlation to IoT web 7.  Migrate from point solution to platform: customer/

partner-added value via software

Example: Universal

Flexible material handling robots • Trained, not programmed • Tight integration of 3D

structured light area sensor with robotic arm control


Four Embedded Vision Opportunities

1.  Better algorithms on existing streams •  tagging and captioning surveillance cameras

2.  Extract new kinds of data from existing streams •  extract fashion trends or weather from user’s

photo libraries, 3.  New business models for existing streams

•  pay-per-success on search or modify 4.  Put cameras in new places

•  Optical gas IR imaging cameras

Example: Blue River Precise herbicide application •  “See and Spray” identifies and

targets weeds, not soil or crop •  “LettuceBot” precision thinning


Embedded Vision Funding

•  Used CrunchBase to estimate embedded imaging, vision & video funding Q2’12-Q1’17

•  Reported rounds: 190 •  Total funding value: $1.23B •  Average round size [count]:

• Seed: $870K [55] • Series A: $8.3M [27] • Series B+C: $16M [13] • Series D: $35M [5]

$432,718,491

$225,132,890 $174,350,000

$147,680,587

$94,904,390

$63,000,000

$47,717,781

EV Funding Dollars by Round Type

Series Unknown

Series A

Series D

Series B

Debt Financing

Series C

Seed

Grant

Angel

Convertible Note

Private Equity

Equity Crowdfunding

Product Crowdfunding


Embedded Vision Funding Growing

55

39 27

15

14

9

7

EV Funding Rounds By Type Seed

Series Unknown

Series A

Debt Financing

Grant

Series B

Angel

Equity Crowdfunding

Series D

Convertible Note

Product Crowdfunding

Series C

Private Equity

Non-equity Assistance

Funding Dollars

Funding Rounds

0

2

4

6

8

10

12

14

16

18

20

$-

$50,000,000

$100,000,000

$150,000,000

$200,000,000

$250,000,000

Q2'

12

Q3'

12

Q4'

12

Q1'

13

Q2'

13

Q3'

13

Q4'

13

Q1'

14

Q2'

14

Q3'

14

Q4'

14

Q1'

15

Q2'

15

Q3'

15

Q4'

15

Q1'

16

Q2'

16

Q3'

16

Q4'

16

Q1'

17

Fund

ing

Even

ts

Fund

ing

Dol

lars

EV Funding Events and Dollars by Quarter


Another Perspective “Cognitive Computing” Startups

Cognitive Computing: 277

Embedded: 82

Vision: 125

Embedded Vision:74

•  Almost ¾ of 276 startups focus on cloud software: CRM, logistics, predictive marketing

•  Heavy emphasis on document and text processing in the cloud

•  Plenty of vision/image processing in the cloud: non-real-time analysis

•  Most of embedded includes vision. Rest is audio, voice and motion sensing


Vision+ Emerging Embedded Segments

Autonomous Vehicles and Robotics

Monitoring, Inspection and Surveillance

Human-Machine Interface

Personal Device Enhancement

Vision Multi-sensor: image, depth, speed Environmental assessment Full surround views

Attention monitoring Command interface Multi-mode ASR

Social photography Augmented Reality

Audio Ultrasonic sensing

Acoustic surveillance Health and performance monitoring

Mood analysis Command interface

ASR social media Hands-free UI Audio geolocation

Natural Language

Access control Sentiment analysis

Sentiment analysis Command interface

Real-time translation Local service bots Enhanced search


Cognitive Embedded Vision Startup Scene •  Identified 74 startups focused on machine learning for embedded vision •  Half in US, >30% in CA. Half doing robots, drones and cars

0 5

10 15 20 25 30 35

Cognitive EV Startups by Country

CO MI PA TX MA

Surveillance

Vehicles

Human-Machine Interface

Drones and Robots

Silicon

0

5

10

15

20

Application for Cognitive EV Startups


The Cognitive Embedded Vision List Abundant Robotics Accelerated Dynamics AIMotive Airware AKA Alchera Technologies Algocian Anki Argo AI Auro Robotics Blue Vision Labs BrainChip Cambricon Cerebras Systems Clearpath Robotics CloudMinds Cognitive Pilot Comma AI

Deep Vision Deep Vision DeepGlint Deephi DeepScale Drive.ai Emotibot Emovu Emteq Evolve Dyanmics Face++ FiveAI Graphcore Horizon Robotics Intuition Robotics Iris Automation Isocline Kindred

Kneron KNUPATH Leapmind Lily Camera Machines with Vision Mashgin Memkite Minieye Momenta MorpX Nauto Netradyne Neurala Novumind Noxton Analytics nuTonomy Osaro Oxbotica Pilot AI Labs

Quanergy Reduced Energy Microsystems RobArt RoboCV Rokid Scortex Shield AI Skydio Sportcaster Tenstorrent TeraDeep ThinCI Third Eye Systems Universal Robotics Velodyne Viz White Matter Zero Zero Robotics Zoox


Observations, Caveats and Warnings

•  Doing a startup is not for the faint-of-heart –  It is an emotional roller coaster – it will test your self-confidence regularly. Nurture your

key relationships –  You will find yourself doing all kinds of unexpected tasks: (cleaning toilets, writing

documentation, doing customer support) –  The likelihood of success is modest –  It’s a marathon, not a sprint – you may need to project rapid success, but it won’t

happen •  Don’t get greedy with the rewards – spreading the equity across the core team

helps essential cohesion. •  Maintain tight focus - chasing too many new ideas is a huge distraction •  Don’t do deep learning just because it’s cool – figure out the real need •  My advice is based on my direct experience and observations of the market, but

necessarily limited, idiosyncratic and biased – take it with a grain of salt


Resources

Check out: www.cogniteventures.com •  The Cognitive Computing Startup List •  The Cognite Blog •  Cognite Resources:

http://www.cogniteventures.com/sample-page/new-resources/ Bradford Cross’s AI Startup Predictions: http://www.bradfordcross.com/blog/2017/3/3/five-ai-startup-predictions-for-2017 Paul Graham on Startups: •  Essay: http://paulgraham.com/before.html •  Video: https://www.youtube.com/watch?v=ii1jcLg-eIQ


Thank you

Documents

How to Start an Embedded Vision Company - Cognite · PDF fileHow to Start an Embedded Vision Company . ... Copyright © 2017 Cognite Ventures LLC 10 ... Convertible Note Private Equity