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
The cybersecurity landscape is undergoing radical transformation through breakthrough technologies that for the first time offer secure and private compute (SPC) to enterprises. We wish to encrypt data across the three phases that track its complete lifecycle: at rest, in flight, and during compute. With encryption of data throughout its lifecycle and with the encryption keys held by and never leaving the data owner, it is possible to offer maximum security and privacy. The challenge has been creating viable encryption in compute schemes – this is what SPC technologies tackle. The usual, current practice in any computation on encrypted data is that first you need to decrypt it, perform the required computation, and then encrypt the results. The challenge and ‘holy grail’ has been to compute with encrypted data and do so within reasonable time and cost.
SPC has emerged in recent years with a number of competing technologies which each have their strengths and weaknesses. This report explains the technology and market landscape and profiles some of the new leading players pioneering this field. We cover technologies such as homomorphic encryption (HE) and its various modes, including fully homomorphic encryption (FHE), as well as enclaves in trusted execution environments (TEE) and applications in secure multi-party computation (MPC). We also name the key organizations that are driving this community, describe the efforts in standardization, and identify business use cases that enterprises should be aware and learn about today. The report further provides profiles on vendors at the cutting edge of SPC: Cornami, Cybernetica, Decentriq, Duality, Inpher, and Zama.
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What you will learn
- How is secure and private compute (SPC) different from other forms of data encryption and what will its impact be on businesses and how we use the internet and cloud.
- What are the key technologies enabling SPC, including an introduction to FHE, TEE and MPC.
- The strengths and weaknesses of the key enabling technologies.
- What is on the roadmap in SPC and what will be the impact of hardware accelerators.
- Which are the key SPC vendor associations and what is their activities.
- We provide in-depth profiles on each participating vendor, who are pioneers in SPC.
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Contents
Kisaco Research View ................................................................................3
Motivation ....................................................................................................3
Key findings .................................................................................................3
SPC technology and market landscape ......................................................4
Introduction to SPC .....................................................................................4
Naming conventions and associations.........................................................4
What is new and possible today that is different from the past ...................4
The business use case for SPC ..................................................................5
The key technologies in SPC ......................................................................6
Lattice-based cryptography .........................................................................6
Fully Homomorphic Encryption (FHE) .........................................................6
Somewhat and Partial Homomorphic Encryption (SHE, PHE) ....................8
Enclaves in TEE...........................................................................................9
Quantum proofing SPC ..............................................................................10
Comparing the SPC cryptographic schemes .............................................10
Hardware acceleration for SPC .................................................................14
The DARPA DPRIVE initiative ...................................................................14
Acceleration with GPUs, FPGAs, and domain specific architectures ........15
Vendor analysis..........................................................................................15
Cornami .....................................................................................................16
Cybernetica ...............................................................................................19
Decentriq ...................................................................................................22
Duality Technologies .................................................................................24 Inpher.........................................................................................................27 Zama..........................................................................................................30
Appendix ....................................................................................................33
The SPC industry associations...................................................................33
Confidential Computing Consortium ..........................................................33
Homomorphic Encryption Standardization ................................................34
MPC Alliance .............................................................................................35
Acknowledgements ....................................................................................35
Author.........................................................................................................35
Kisaco Research Analysis Network ...........................................................36
Copyright notice and disclaimer .................................................................36
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Figures
Figure 1: Securing the data lifecycle: the problem without encrypting compute.
Figure 2: Lattice-based cryptography.
Figure 3: HE operations of addition and multiplication on data items.
Figure 4: FHE breakthrough: bootstrapping.
Figure 5: TEE.
Figure 6: Comparing HE, TEE, and secure sharing MPC
Figure 7: FHE compute time using LAWS
Figure 8: The vendors profiled in this report and key technologies they offer.
Figure 9: Comparing a simple 1D convolution operation on a CPU, GPU, and Cornami chip.
Figure 10: The Cornami software stack.
Figure 11: Configuring the hardware to the “shape of the software”.
Figure 12: Sharemind: building services with end-to-end data protection.
Figure 13: Comparing the two versions of Sharemind.
Figure 14: The Decentriq platform: high-level workflow and components for the MPC example.
Figure 15: Duality products and application areas.
Figure 16: Inpher entry in the IDASH Privacy and Security Workshop 2020.
Figure 17: Inpher XOR solution.
Figure 18: The Zama software stack for FHE.
Figure 19: Venn diagram of various technologies used to protect data in use.
Figure 20: MPC concerns two or more parties wishing to share information confidentially
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FAQs
1. What is the Kisaco Innovation Radar report (KIR)?
In this report we cover a topic that is still nascent in the vendor market and not ready for a KLC report. We introduce the topic and cover several leading vendors with profiles, and where possible we include in our coverage a heatmap of key high-level features in the technology being used. Vendors profiled are selected for their pioneering contribution to the field.
2. 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.
3. 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.
4. 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.
5. 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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