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Artificial Intelligence (AI) Accelerator Market Guide for Venture Capitalists: Investment Trends, Technological Insights, High-Growth Potential Markets, Promising Startups, & Forecasts 2018-2025

Report Code : IE41834
Published: April 2019

Report Description Table of Contents Ask The Analyst Ask For Methodology

Market Essence
Joseph Schumpeter, an Austrian economist, economic historian & capitalist developed innovation theory of trade cycles in 1942. According to Schumpeter’s ‘Creative Destruction’ theory, creative destruction is a process in which new technologies, new products, new methods of production and distribution make old ones obsolete, forcing existing entrepreneurs to quickly adapt to a new environment or fail. Schumpeter’s theory is still appropriate in the Artificial Intelligence era. Industry has seen striking growth in the field of Artificial Intelligence (AI) over the past decade. The technology isn’t completely new to us; though the term “AI” initially coined in 1955, scholars have been originating it from centuries. AI and Internet of Things (IOT) have propelled industrial revolution 4.0, which is projected to completely transform our lives. Considering major developments in the AI, Machine Learning (ML), and Deep Learning (DL) technologies, Industry Essence has published several technology and market research reports in the past, covering AI hardware, software, and service markets. However, after conducting over 400 primary interviews for our AI and associated technology research reports, our team identified a need to offer a comprehensive market and technological insight research report, particularly for investors. Just to be sure, we then connected with around 52 Venture Capitalists (VCs) over the globe and discussed on rapidly growing AI, ML, & DL technology markets, VCs role, investor specific market insight needs, technological research needs, and on other associated areas. From these discussions, we learn the need to work on a specific syndicate research report related to- AI accelerators/chipsets for VCs, angel investors, incubators, corporate investors, banks, investment bankers, and government agencies. 
AI performs complex tasks of learning and cognition at a level that matches or exceeds that of human performance; therefore it’s a distinct technology from the perspective of business models and value creation. Every time a new technology has successfully developed in the AI market, it has been tagged as a complete new industry, for example, search engines, speech recognition, voice recognition, industrial robotics, autonomous vehicles, computer vision, and many others. Rapid emergence of AI technology is majorly driven by six factors- development of advanced AI algorithms, easy availability of vast data to train AI systems, development of technologically advanced processors and Accelerators for AI training and inference processes, rising datacenter based AI services by technology companies for Machine Learning and Deep Learning engineers and developers, rapidly developing Open Source AI software platforms, and growing investments in AI technology by investors. Globally business leaders have great expectations from the AI technology than any other emerging technologies, including Internet of Things (IoT), Blockchain, and Augmented and Virtual Reality.
The AI technology adoption rate was tripled globally in 2018, with one in seven companies have adopted the technology. Maturing AI technology, increasing awareness and investments, falling technology cost, and easy availability of open source APIs are motivating the enterprises to adopt the technology quickly. Chinese companies are frontrunners in terms of AI adoption rate, around 31% companies have already adopted the AI technology in some form in mainstream operations and rests are rapidly finding their ways to implement it. US ranked second with 23% AI adoption rate, followed by  India, UK, France, Germany, Japan, and many others.
Considering the huge potential of AI to create a massive impact on global economies in the coming years, investors are aggressively investing in AI technology startups. In 2018, Venture Capitalists (VCs) invested around 25 Billion USD worldwide in AI technology startups. In past six years, the market has witnessed hefty million dollar investments in AI startups.
In the global AI technology market, over 150 VCs are actively investing in AI startups. Some of these are Google Ventures, Y Combinator, Kleiner Perkins Caufield & Byers, Data Collective, New Enterprise Associates, Accel, Norwest Venture Partners, Techstars, Khosla Ventures, and Intel Capital among many others.
AI startups use these investments for further R&D and product enhancement, talent acquisition and retention, global business expansion, and marketing activities- market share acquisition. Though the VCs are raising large capital to invest in AI technologies, Industry Essence analyst team has critically analyzed the AI trend- AI just a technological hype or it will actually deliver what it promises?
 AI Accelerator market offers range of attractive investment opportunities for VCs. The AI technology market can be segmented by different AI accelerators by infrastructure, technology types, chipset type, processors, by technology types, innovative AI computing, end-user industry applications, regions and countries. Industry Essence is analyzing these market segments and potential investment opportunities for VCs.
While conducting primary interviews, we came across different challenges faced by VCs and startups at different stages, our analyst team has analyzed and offered solutions and recommendations on these topics.
 
In this research report, we comprehensively analyze AI Accelerator market including AI network and memory segments and cloud/datacenter & on edge accelerators. The report will definitely add a great value at multiple decision points for VCs, right from primary market understanding to VC investment decisions at various funding stages in AI startups. There are more than 5000 startups worldwide, developing AI, ML, DL, and other intelligent technologies. In this report, we are analyzing entire market ecosystem, analyzing VCs (investors), Intellectual Property (IP) vendors, technology giants, startup ecosystem, IC & semiconductor companies, and end industry users.
The AI data center accelerator market was valued at USD 2.84 Billion in 2017 and it is projected reach USD 99.1 Billion by 2025, at a CAGR of 55.8% between 2018 and 2025. In this revamp report, we are increasing our growth forecasts (CAGR) from 46.1% to 55.8% CAGR from 2018 to 2025. The revision is made due to the significant growth in new product launches in early 2018. The report qualitatively and quantitatively analyzes revenue drivers, restraining factors, and opportunities present for active companies in this market. In the coming three to four years, the AI market is projected to grow exceptionally due to the rising demand for AI solutions at edge/device computing. We analyze the market attractiveness with help of Porter’s five forces model. This model is based on the qualitative and quantitative inputs from primary respondents and secondary data sources such as annual reports, press releases, company websites, and Industry Essence’s paid databases. This model analyzes most influential factors for the market and assists companies in taking key decisions such as exploration of investments avenues, expansion plans, production enhancement, market strategy building, pricing and product positioning, revenue enhancement, and deciding backward/forward integration steps. 
 
AI functions- training and inference are performed either on cloud or at device level (on edge) or in some cases partially on cloud and edge. We’ve segmented AI accelerator market based on the infrastructure computing- data center and on edge. Currently more than 50% AI operations (training and inference) are performed by cloud based accelerators, however, with development of advanced AI processors for smartphones, tablets, automotive vehicles (AV), smart speakers/devices, fitness bands and healthcare devices, Head Mounted Displays (HMDs), drones, robotics, and many other devices, the market for on edge accelerators will increase exceptionally in the years to come. In this report, we have segmented AI Data Center Accelerator Market by processor types- Central Processing Unit (CPU), Graphics Processing Unit (GPU), Field-programmable Gate Array (FPGA), and Application-specific Integrated Circuits (ASICs). The market for ASICs is projected to grow at a highest CAGR of 91.9% between 2018 and 2022. ASICs have the best performance, power, and efficiency in performing specific applications and therefore they’re expected to be employed in most of the AI data centers.
 
Hey! Have you already invested in AI accelerator/chipset company/companies and looking to invest in a technology pioneer? Yes, there’s plenty of room! In past couple of years, few startups are innovating at architecture level to accelerate neural networks to next level. It includes optical computing, analog computing, Processing in Memory (PiM), and neuromorphic computing architectures. These startups are projected to disrupt the market exceptionally in the coming years, therefore VCs can go a step further and invest in these multi-billion dollar technologies. We’ve analyzed a range of promising startups working on these innovative technologies and profiled them in the report.
AI Data Center Accelerator Market is segmented on the basis of technologies- machine learning, natural language processing, vision computing, and contextual computing. Machine learning market is further sub segmented as- deep learning, supervised learning, un-supervised learning, and reinforcement learning among others.
 
 
AI technology is used in various applications around the globe and therefore accelerator market is further segmented as end user industries- agriculture, automotive, FinTech, healthcare, human resources, law, manufacturing, marketing, retail and security. In 2017, more than 30% market was occupied by marketing industry wherein more than 60% market value was attributed to search advertising and social media advertising. In this report, we are comprehensively covering all the sub segments of these industries. AI accelerator market in marketing industry was valued at USD 0.94 Billion in 2017 and it is projected to reach USD 20.17 Billion by 2025 at a CAGR of 45.5%.
 
Global AI accelerator markets are covered in the report- North America, Europe, APAC, Latin America, and Middle East and Africa. The report further sub segments regional markets by major countries including- US, Canada, Mexico, UK, Germany, France, Italy, Spain, China, Japan, India, Korea, Middle East & Africa, and Latin America among others. North America is the largest market for AI accelerators, largely dominated by US. Most of the AI technology giants are headquartered at US such as- Intel Corp (US), Nvidia (US), Google LLC (US), IBM Corporation (US), Apple Inc (US), Qualcomm Inc (US), and many others along with many startups. China is one of the major AI technology adopter with many AI software startups and with recent government support many organizations have started developing in-house chipsets. 
 
The market for Artificial Intelligence (AI) accelerator market is highly competitive due to the presence of many Tier I, Tier II companies, and startups competing on the basis of innovation and product positioning. In 2016, there were less than 20 companies in AI processor market however; by the end of 2018 there are more than 100 companies that are competing in the space around the globe. Apart from US market, which dominates silicon chipset manufacturing business; there are new startups around the globe, serving range of different AI applications by innovation. In this report we have analyzed key organic and inorganic strategies adopted by different companies between 2013 and 2017 to remain competitive in the market. This section helps our clients to understand competitor strategies well and in adapting market needs quickly. The report profiles major companies and their thorough analysis (it includes* business summary, key financials, business diversification, market competence, expansion strategies, and SWOT analysis of major companies).
Industry Essence ‘Vision Matrix’ analyze and position market participants based on two parameters- market competitiveness of product (based on many sub-factors) and growth strategy execution capability (based on many sub-factors). We’ve given snapshots from the report here; you can find the detailed analysis in the report.
 
List of Venture Capitalists actively investing in AI & Associated Technologies
500 Startups
Accel Partners
AI Capital
Alpine Technology Fund
Amadeus Capital Partners
AME Cloud Ventures
Andreessen Horowitz
ASGARD
Balderton Capital
Battery Ventures
Bessemer Ventures
Bloomberg Beta
Bpifrance
C4 Ventures
Charles River VC
Citi Ventures
Cognitive Ventures
Combient AB
Comcast Ventures
Comet labs
Data Collective
Dell Technologies Capital
Enterprise Ireland
Entrepreneur First
Ericsson Ventures
Felicis Ventures
First Round
FirstMark
Founder Collective
Founders Fund
GE Ventures
General Catalyst
GGV Capital
Google ventures
Horizons Ventures
IA Ventures
In-Q-Tel
Intel Capital
Khosla Ventures
Kima Ventures
LDV capital
Lightspeed Venture Partners
Localglobe
London Co-Investment Fund
Lux Capital
M12 VC (Microsoft)
Madrona Venture Group
Mahindra Partners
Motorola Solutions Venture Capital
New Enterprise Associates
Octopus Ventures
Passion Capital
Pi Ventures
Plug and Play
Qualcomm Ventures
Rakuten Ventures
Real Ventures
Robert Bosch VC
RRE Ventures
SAIC Capital
Salesforce Ventures
Samsung Ventures
SEED Capital Denmark
Seedcamp
Sequoia Capital
Social Starts
Softbank
SOSV
Sunstone Capital
SV Angel
Techstars
Tencent
Touchstone Innovations
True Ventures
Two Sigma Ventures
Wipro Ventures
Y Combinator
Zeroth AI
Zetta Venture Partners
ZhenFun

Table of Contents
1. Global investment trends in Artificial Intelligence (AI) market: Regional and country specific trends
1.1. Major Investments and Venture Capitalists in AI Market
1.2. Active Venture Capitalists in AI Market
 
2. Artificial Intelligence Technology Timeline: 1956 - 2018
 
3. Investor’s Perspective: Venture Capitalists Survey January 2019
3.1. Technological Investment Preference?
3.2. AI Hardware or Software?
3.3. High Risk- High Return?
 
4. Hefty Investments in AI Technology: A Hype or Rational?
4.1. Why AI startups need VC investments?
4.1.1. Global expansion & need to gain market share
4.1.2. Product enhancements and R&D Costs
4.1.3. Talent acquisition and retention
 
5. Lucrative Investment Opportunities for Venture Capitalists
5.1. Recommendations for Investors and investees
 
6. Venture Capitalists ‘Pain Points’ in AI Accelerator Market
6.1. Recommendations for investors and investees
 
7. Is it hard to ‘Exit’ an AI Accelerator/Hardware Startup?  
 
8. Value & Supply Chain and Market Ecosystem Analysis of AI Accelerator Market
8.1. Intellectual Property (IP) Vendors
8.2. Technology Giants
8.3. Startup Ecosystem
8.4. IC & Semiconductor Companies
8.5. End industry users
8.6. Analyst recommendations for investors and investees
 
9. What’s there for AI Accelerator Companies?
9.1. Revenue Driving Factors for Artificial Intelligence (AI) Accelerator Market in Next Five to Ten Years
9.2. Restraints Affecting Growth of the Artificial Intelligence (AI) Accelerator Market
9.3. Lucrative Opportunities for Artificial Intelligence (AI) Accelerator Market Stakeholders
 
10. Analysis of Organic and Inorganic Business Strategies by AI Companies, 2013-2017
10.1. Organic Strategies Adopted by Companies to Remain Competitive
10.2. Inorganic Strategies Adopted by Companies to Remain Competitive
10.3. Analyst Insights & Recommendations
11. Market Attractiveness Assessment: Porter’s Five Forces Market Analysis
11.1.1. Bargaining Power of Suppliers
11.1.1.1. Number of Suppliers
11.1.1.2. Switching Costs
11.1.1.3. Forward Integration
11.1.1.4. Product Differentiation
11.1.2. Intensity of Competition
11.1.2.1. Number of Competitors
11.1.2.2. Product Differentiation
11.1.2.3. Switching Costs
11.1.2.4. Rate of Industry Growth 
11.1.3. Threat of New Entrants
11.1.3.1. Entry Barriers
11.1.3.2. Capital Requirement
11.1.3.3. Technology Knowhow
11.1.4. Threat of Substitutes
11.1.4.1. Availability Of Substitutes
11.1.4.2. Switching Costs
11.1.4.3. Convenience
11.1.4.4. Quality
11.1.5. Bargaining Power of Buyers
11.1.5.1. Number Of Buyers
11.1.5.2. Switching Costs
11.1.5.3. Availability Of Substitutes
11.1.5.4. Product Differentiation
11.1.5.5. Price Sensitivity
 
12. Market Opportunity Assessment for VCs: Artificial Intelligence (AI) Accelerator Market, By Infrastructure
12.1. On Data Center/Cloud (Market Size, Trends, Forecasts, and Analysis)
12.1.1. Shifting AI Controls From Cloud/Data Center To On-Edge 
12.1.2. Processors (Market Size, Trends, Forecasts, and Analysis)
12.1.3. Network Component (Market Size, Trends, Forecasts, and Analysis)
12.1.4. Memory and Storage (Market Size, Trends, Forecasts, and Analysis)
12.1.4.1. Need To Adopt And Develop AI Enabled Memory/Storag
12.1.4.2. On-Chip Memory, HBM, DDR5, and GDDR (Cold, Warm, and Hot Data Needs)
12.2. On Edge (Market Size, Trends, Forecasts, and Analysis)
12.2.1. Smartphones And Tablets 
12.2.2. Automotive
12.2.3. Drones
12.2.4. Smart Speakers/Devices
12.2.5. Head-Mounted Displays
12.2.6. Security Cameras
12.2.7. Internet of Things (IoT) & Robotics
12.2.8. Healthcare Devices
12.3. Increasing need of AI Compatible on-device Memory
12.4. Promising Startups and Companies and Analyst Insights
 
13. Market Opportunity Assessment for VCs: Artificial Intelligence (AI) Accelerator Market, By Chipset Type
13.1. Training Chips
13.2. Inference Chips  
13.3. Promising Startups & Companies and Analyst Insights
 
14. Market Opportunity Assessment for VCs: Artificial Intelligence (AI) Data Center Accelerator Market, By Processor Type (Market Size, Trends, Forecasts, and Analysis)
14.1. CPU 
14.2. GPU
14.3. FPGA 
14.4. ASIC 
14.5. Promising Startups & Companies and Analyst Insights
 
15. Market Opportunity Assessment for VCs: Artificial Intelligence (AI) Data Center Accelerator Market, By Technology (Market Size, Trends, Forecasts, and Analysis)
15.1. Machine Learning
15.1.1. Supervised Learning
15.1.2. Deep Learning
15.1.3. Unsupervised Learning
15.1.4. Reinforcement Learning
15.1.5. Others
15.2. Natural Language Processing
15.3. Contextual Computing
15.4. Vision Computing
15.5. Analyst Insights
 
16. Few Startups are Innovating Architectures, Will They Sustain in the Longer Run?
16.1. Optical Computing
16.1.1. Active Startups
16.2. Analog Computing
16.2.1. Active Startups
16.3. Processing in Memory (PIM)
16.3.1. Active Startups
16.4. Neuromorphic Computing
16.4.1. Active Startups
 
17. Attractive End-user Industries to Invest: Artificial Intelligence (AI) Data Center Accelerator Market, By End-Use Industry (Market Size, Trends, Forecasts, and Analysis)
17.1. Agriculture
17.1.1. Precision Farming
17.1.2. Livestock Monitoring
17.1.3. Drone Analytics
17.1.4. Agricultural Robots
17.1.5. Others
17.2. Automotive
17.2.1. Autonomous Driving
17.2.2. Human–Machine Interface
17.2.3. Semiautonomous Driving
17.3. FinTech
17.3.1. Virtual Assistant
17.3.2. Business Analytics & Reporting
17.3.3. Customer Behavior Analytics
17.3.4. Others
17.4. Healthcare
17.4.1. Patient Data & Risk Analysis
17.4.2. Precision Medicine
17.4.3. Inpatient Care & Hospital Management
17.4.4. Medical Imaging & Diagnostics
17.4.5. Drug Discovery
17.4.6. Virtual Assistant
17.4.7. Wearable
17.4.8. Research
17.5. HR
17.5.1. Virtual Assistant
17.5.2. Sentiment Analysis
17.5.3. Scheduling Group Meetings and Interviews
17.5.4. Personalized Learning and Development
17.5.5. Applicant Tracking & Assessment
17.5.6. Employee Engagement
17.5.7. Resume Analysis
17.6. Law
17.6.1. eDiscovery
17.6.2. Legal Research
17.6.3. Contract Analysis
17.6.4. Case Prediction
17.6.5. Compliance
17.6.6. Others
17.7. Manufacturing
17.7.1. Material Movement
17.7.2. Predictive Maintenance and Machinery Inspection
17.7.3. Production Planning
17.7.4. Field Services
17.7.5. Reclamation
17.7.6. Quality Control
17.8. Marketing
17.8.1. Search Advertising
17.8.2. Dynamic Pricing
17.8.3. Virtual Assistant
17.8.4. Content Curation
17.8.5. Sales & Marketing Automation
17.8.6. Analytics Platform
17.8.7. Others
17.9. Retail
17.9.1. Product Recommendation and Planning 
17.9.2. Customer Relationship Management 
17.9.3. Visual Search 
17.9.4. Virtual Assistant 
17.9.5. Price Optimization 
17.9.6. Payment Services Management 
17.9.7. Supply Chain Management and Demand Planning 
17.9.8. Others
17.10. Security
17.10.1. Product Recommendation and Planning 
17.10.2. Customer Relationship Management 
17.10.3. Visual Search 
17.10.4. Virtual Assistant 
17.10.5. Price Optimization 
17.10.6. Payment Services Management 
17.10.7. Supply Chain Management and Demand Planning 
17.10.8. Others 
17.10.9.
17.11. Analyst Insights
 
18. Attractive Economies to Invest: Artificial Intelligence (AI) Data Center Accelerator Market, Regional Analysis
18.1. North America (Country Market Size, Trends, Forecasts, and Analysis)
18.1.1. U.S
18.1.2. Canada
18.1.3. Mexico
18.2. Europe (Country Market Size, Trends, Forecasts, and Analysis)
18.2.1. Germany
18.2.2. France
18.2.3. UK
18.2.4. Italy
18.2.5. Spain
18.2.6. Rest Of The Europe
18.3. Asia-Pacific (Country Market Size, Trends, Forecasts, and Analysis)
18.3.1. China
18.3.2. India
18.3.3. South Korea
18.3.4. Japan 
18.3.5. Rest Of The Asia-Pacific
18.4. Middle East & Africa (Country Market Size, Trends, Forecasts, and Analysis)
18.4.1. Israel
18.4.2. U.A.E.
18.4.3. Rest of Middle East & Africa (MEA)
18.5. Latin America (Country Market Size, Trends, Forecasts, and Analysis)  
19. Promising Startups and Companies in AI Market: Vision Matrix
19.1. AI Accelerator Market Assessment (Growth Strategy Execution Capability & Market Competitiveness of AI Product)  
19.1.1. Aspirers
19.1.2. Influencers
19.1.3. Pioneers
19.1.4. Front-runners
19.2. AI Accelerator Intellectual Property (IP) Market Assessment (Growth Strategy Execution Capability & Market Competitiveness of AI Product)
19.2.1. Aspirers
19.2.2. Influencers
19.2.3. Pioneers
19.2.4. Front-runners
 
20. Market Participant Profiles (AI Accelerator Suppliers)
20.1. Adapteva
20.2. AMD Inc.
20.3. Apple Inc.
20.4. Cambricon
20.5. Fuzhou Rockchip
20.6. General Vision
20.7. Google LLC
20.8. Graphcore
20.9. Horizon Robotics
20.10. Huawei-Hisilicon
20.11. IBM
20.12. Intel
20.13. Kneron
20.14. Knowm
20.15. Koniku
20.16. Krtkl Inc.
20.17. Mediatek
20.18. Nvidia
20.19. Qualcomm Inc.
20.20. Samsung
20.21. ThinCI
20.22. Wave Computing
20.23. Xilinx Inc.
21. Market Participant Profiles (AI Accelerator Intellectual Property Suppliers)
21.1. ARM Ltd
21.2. Cadence
21.3. CEVA Inc
21.4. Imagination Tech.
21.5. Synopsys Inc.
21.6. Verisilicon
21.7. Videantis

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