About the Author
Michael Azoff
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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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.
Following the success of general-purpose computing on graphical processing units (GPGPUs) in 2010, a market initiated by Nvidia and helping the company triple its size, the next decade has seen the market explode in investment funding into startups aiming to capture a share with innovative AI focused architectures. In our series of AI hardware accelerator vendor comparisons, we focus here on data centers (DCs) and the high-performance computing (HPC) community.
In this report Kisaco Research provides two Kisaco Leadership Charts (KLCs) 2020-21: one for DC/HPC AI training and one for ED/HPC AI inferencing, with full profiles and assessments of all participating vendors. -
What you will learn
- The attributes of the DC & HPC markets and how they differ from the rest of the market.
- Who are the key players in the DC & HPC market, with deep profiles of nine of our participating vendors competing in AI training and AI inferencing, including strengths and weaknesses?
- Two analyst charts, the Kisaco Leadership Chart (KLC), on the participating vendors: one for AI training and one for AI inferencing. These are the first such charts ever produced in the AI chip market.
- What are the attributes of AI chips in the DC/HPC space versus other use cases in the market? Variables such as energy consumption, cost, precision and more.
- The importance of software in overall AI system performance. The best model accuracy is not necessarily the model per chip combination with highest operations per second.
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Contents
Kisaco Research View. 2
Motivation. 2
Note: Definitions are important 2
Key findings. 2
Companion reports. 3
Solution Analysis: Training and inferencing in the data center and for HPC applications. 3
Market trends. 3
Market size. 3
Training in the DC. 4
Inferencing in the DC. 4
HPC community. 4
AI accelerator characteristics. 5
Precision. 5
Operations (model complexity) 6
AI accelerator memory. 6
Impact of batch size. 7
Power issues. 7
Market segments. 8
Solution analysis: vendor comparisons. 8
Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Data Centers and HPC. 8
Introduction. 8
Data center and HPC: AI accelerator vendor comparisons. 10
The KLC charts for AI hardware accelerators: DC and HPC. 11
Edge and /automotive (a companion report) 14
Vendor analysis. 15
Cerebras, Kisaco evaluation: Innovator 15
Kisaco Assessment 17
Graphcore, Kisaco recommendation: Innovator 17
Kisaco Assessment 20
Habana Labs (an Intel company), Kisaco evaluation: Innovator 20
Kisaco Assessment 22
Imagination Technologies, Kisaco evaluation: Contender 23
Kisaco Assessment 25
LightOn, Kisaco recommendation: Emerging Player 26
Kisaco Assessment 28
Kalray, Kisaco recommendation: Innovator 29
Kisaco Assessment 31
Mythic, Kisaco evaluation: Innovator 32
Kisaco Assessment 34
Nvidia, Kisaco evaluation: Leader 35
Kisaco Assessment 39
Xilinx, Kisaco evaluation: Leader 40
Kisaco Assessment 42
Appendix. 43
Vendor solution selection. 43
Inclusion criteria. 43
Exclusion criteria. 43
Methodology. 43
Definition of the KLC. 43
Kisaco Research ratings. 44
Further reading. 44
Acknowledgements. 44
Author 44
Copyright notice and disclaimer 44
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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
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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.
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About the Author
Michael Azoff
Chief AnalystKisaco ResearchWith 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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