KLC Hardware Accelerators 2020-21 (Part 1): Technology and Market Landscapes, July 2020 | Kisaco Research

About the Author

Author:

Michael Azoff

Chief Analyst
Kisaco Research

With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst. 

Eitan Michael Azoff, PhD, MSc, BEng.

HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.

In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting, and wrote a book on the topic for publisher John Wiley & Sons in 1994.

Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.

My analyst coverage areas at KR Analysis

My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.

Our analysis showed that the market naturally fell into three areas of hot activity:

▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.

▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.

▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).

We produced four Kisaco Leadership Charts out of this research.

We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.

Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.

  • Motivation

    Artificial intelligence (AI) is out of the research laboratory and is in the realm of practical engineering applications. AI engineering today is largely about running machine learning (ML) models on digital computers, and these models are typically simulations of brain-inspired models such as neural networks, with deep learning (DL) being the most successful example today. With the plateauing out of CPU performance improvements and the end of Moore’s law, even with multi-core CPU machines, the community has turned to hardware accelerators to run their AI models.

    This part 1 report provides an overview of the players in the AI hardware accelerator market, we span established players to the startups aiming to capture a market share worth over $5b today. Our analysis of the players in the market includes an overview of AI hardware architectures available, and how the market segments itself. In parts 2 and 3 we provide the Kisaco Leadership Charts (KLCs) with vendor product comparisons.

    In this report Kisaco Research provides snapshot profiles on 80 startups and 34 established players in the AI hardware accelerator space.

  • What you will learn

    • The makeup of the AI chip market in terms of types of accelerators being developed by startups and those already in the market.
    • The number of players in the market by geography and by type of business: startup or established. We also identify 14 startups still in stealth mode. We provide data on investment going into the startups, split by geography.
    • The strategy necessary for a startup to rise above the incumbents.
    • What it takes to compete effectively in the market: the need for a holistic approach, hardware and software.
    • How the AI chip market clusters around three strong demand areas. 
    • For anyone new to the AI space, we provide a summary of AI history and our level setting guide to where we are now in terms of long-term research aims in AI.
  • Contents

    Kisaco Research View. 2

    Motivation. 2

    Definitions are important 2

    Key findings. 2

    Market landscape: AI hardware accelerator trends. 3

    Positioning the role of AI hardware accelerators. 3

    AI hardware accelerator market overview. 4

    AI architecture trends. 6

    Vendor market overview. 8

    AI hardware accelerator market: segments. 8

    Introduction. 8

    Accelerators targeting data centers and HPC. 8

    Accelerators targeting automotive industry. 9

    Accelerators targeting consumer electronics, IOT, and edge computing. 10

    Accelerators in other markets: workstations, engineering, and mobile. 10

    Types of accelerators. 10

    AI hardware accelerators: benchmarks. 11

    AI hardware accelerators: connecting with software. 12

    AI hardware accelerators: vendor patterns. 13

    AI: building and deploying ML software applications. 14

    Regional factors. 14

    AI hardware accelerator market: vendor snapshots. 16

    Market overview. 16

    Startups. 17

    Startup: AI hardware is part of the core business (Type A) 17

    Startups: AI hardware is a non-core business (Type B) 24

    Established companies producing AI processors. 25

    Established: AI hardware is part of the core business (Type A) 25

    Established: AI hardware is a non-core business (Type B) 28

    Appendix. 29

    AI technology level setting: the state of the art today. 29

    A short history introducing essential concepts. 29

    Level setting: definitions. 30

    AI technology: near term.. 31

    AI technology: longer term and AI winters. 32

    Defining AI training and inference modes. 34

    Training neural networks. 34

    Inference mode. 34

    Report methodology. 34

    Acknowledgements. 34

    Further reading. 35

    Author 35

    Copyright notice and disclaimer 35

  • Figures

    Figure 1: Relating AI, hardware, and data science

    Figure 2: Nvidia GPU architecture history

    Figure 3: AI accelerator architecture runway

    Figure 4: AI application power characteristics and use cases

    Figure 5: AI applications and characteristics of AI accelerators

    Figure 6: Types of AI hardware accelerators

    Figure 7: Type A startup AI hardware accelerator players by geography

    Figure 8: Type B startup AI hardware accelerator players by geography

    Figure 9: Type A established AI hardware accelerator players by geography

    Figure 10: Type B established AI hardware accelerator players by geography

    Figure 11: Startups and established players by numbers and startup funding

    Appendix

    Figure 12: Deep Learning and how the research segments in AI relate to each other

    Figure 13: The current state-of-the-art in AI we call Machine Intelligence

    Figure 14: How AI researchers define AI

  • FAQs

    1.  What is the KLC?

    The Kisaco Leadership Chart (KLC) is KR Analysis’s take on the classis industry analyst chart in which vendor products are assessed and their scores plotted on a chart comprising four quadrants: Leader, Contender, Innovator, and Emerging Player. The x-axis represents strength of technical features, the y-axis the strength of market execution and strategy, and the size of plotted circle represents market revenue normalized to the strongest participating player in the research.

    In researching the KLC we receive privileged information from a vendor. As explained in question 3, participating vendors are actively engaged in our research. Confidential privileged vendor information is not disclosed in our report but helps us assess vendors in our analysis.

    2. What is the vendor selection process for a KLC project?

    KR Analysis creates a shortlist of vendors to invite to the research project. The aim is to include the leading players as well as innovative smaller players, across startup and established vendors. KLC research can at best be representative of the market and is not designed to be exhaustive – in some markets the sheer number of players would make an exhaustive KLC unmanageable, in smaller markets we are still dependent on vendors agreeing to participate.

    We do create KR Analysis Technology and Market Landscape reports in which we typically list the players in the markets with thumbnail profiles providing information such as company leadership, location, funding status, and main product(s) details. While we cannot guarantee exhaustiveness, the landscape report does aim to list the most important vendors and does not require vendor participation.

    3. In a KLC, what does participating entail for a vendor?

    First of all, we do not charge vendors to participate in a KLC. Participating vendors need to be actively engaged in a KLC research project, this involves completing a comprehensive questionnaire, which we score and use as the basis for positioning the vendor in the report’s KLC. We also hold a deep dive briefing and engage in plenty of Q&A. Finally, we research publicly available material on the vendor and its product(s) to complete our final view of the vendor. 

    4. Why are some notable vendors missing from the report?

    As explained in question 2, we do invite the leaders in a market segment we are researching, however not all such players agree to participate. As explained in question 3, participating involves active engagement and example reasons vendors offer for declining our invitation are, often ending with “...but please consider us next cycle of the report.”:

    • We are in the midst of an event in which our relevant staff do not have the time to engage in your process.
    • We are going through a major change in strategy or product re-architecture and the timing is not right for us to participate.
    • We are about to have our IPO and this is not the right time to participate.
    • We are about to launch our flagship product and the report timing is not right for us.
  • About the Author

    Author:

    Michael Azoff

    Chief Analyst
    Kisaco Research

    With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst. 

    Eitan Michael Azoff, PhD, MSc, BEng.

    HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.

    In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting, and wrote a book on the topic for publisher John Wiley & Sons in 1994.

    Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.

    My analyst coverage areas at KR Analysis

    My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.

    Our analysis showed that the market naturally fell into three areas of hot activity:

    ▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.

    ▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.

    ▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).

    We produced four Kisaco Leadership Charts out of this research.

    We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.

    Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.

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