Over the past decade, artificial intelligence has moved from laboratories to industrialization, triggering a new round of technological revolution globally. While AI is rapidly integrating into niche scenarios, it is also constantly reshaping traditional industry models, delivering unique future value to the economy and society.Frost & Sullivan (hereinafter referred to as 'Frost & Sullivan') has released the latest 'China Deep Learning Software Framework Market Research Report (2021)', which provides an in-depth investigation and analysis of the Chinese deep learning software framework market in 2021.
Deep learning is a key technology driving the current AI explosion, and deep learning software frameworks are the underlying development tools to solve the 'difficulty in implementation' problem associated with this technology. Currently, deep learning technology has entered an upgraded and optimized phase, driving the related industries to develop rapidly. This report aims to explore the core driving factors of the industry through the analysis of its development history and industrial chain, dissect the current market trends in the deep learning software framework industry, and construct a vendor competitiveness system based on three key dimensions: application, product, and ecosystem. It evaluates the core competitive advantages and comprehensive barriers of mainstream players, forms an objective assessment of the development of deep learning software frameworks in the Chinese market, and provides reference suggestions and key inspirations for the future development of the industry.
Empowering the entire value chain from upstream to downstream, and the deep learning software framework enters a stage of accelerated development.
A deep learning software framework is a tool that enables the rapid construction of deep learning models by modularizing algorithms. As an underlying development tool, it can effectively lower the technical threshold for AI application development and help break through development bottlenecks in deep learning technology. From the perspectives of task undertaking, deployment location, and workflow, the subtypes of deep learning software frameworks have different key technical requirements, providing various strategic entry points for manufacturers.
Looking at the development history of deep learning software frameworks, it can be roughly divided into three stages: initial establishment, adjustment, and accelerated development. Starting from 2014, various research and development entities in the global artificial intelligence academic and industrial communities gradually opened source their independently developed deep learning software frameworks, and built open artificial intelligence development platforms based on these frameworks to promote the establishment of an artificial intelligence industry ecosystem. Among them, two deep learning software frameworks, TensorFlow developed by Google's team and PyTorch developed by Meta's team, occupy a relatively dominant position in the industry.
At the same time, China is rapidly deploying systematic layouts for open-source development frameworks. Self-developed open-source deep learning software frameworks such as Baidu PaddlePaddle, Tencent YouTu NCNN, Huawei MindSpore, and Alibaba XDL are being upgraded at an accelerated pace. Among them, Baidu PaddlePaddle, one of the earliest open frameworks launched in China, has been widely applied in business scenarios such as intelligent manufacturing, intelligent finance, and intelligent healthcare. Currently, with the advancement of mainstream vendor product updates, deep learning software frameworks have entered a stage of accelerated development.
Source: Frost & Sullivan consulting
The deep learning software framework industry chain mainly consists of upstream hardware, midstream framework developers, and downstream application scenarios. The upstream is the foundation for deep learning implementation, primarily including computing hardware (chips), which provide algorithmic and computational support for deep learning software frameworks. The midstream is the core of deep learning software frameworks, starting from simulating human intelligence-related features to construct application technical paths, mainly including speech recognition, computer vision, and natural language processing. The downstream mainly involves the application of deep learning software frameworks in various sub-scenarios, including manufacturing, security, finance, healthcare, retail, transportation, logistics, agriculture, and other fields. In the future, upgrades in upstream basic computing, maturation of midstream application technologies, and integration of downstream application scenarios will deeply empower their subsequent development.
Source: Frost & Sullivan consultancy
Focusing on core drivers at three major levels, China's strategy provides fertile ground for growth
Based on industrial chain empowerment, the driving factors for the next phase of the deep learning software framework industry mainly include three levels.
First, the application scenarios are becoming increasingly clear, and large-scale applications are emerging as a dawn in the horizon.The increasing maturity of application technologies is driving the penetration of deep learning software frameworks into various scenarios. Large-scale applications will bring potential incremental growth to the industry. According to Frost & Sullivan statistics, in 2021, there were a total of 158 financing events worth over 100 million yuan in the AI field. Among them, the financing numbers for autonomous driving, smart healthcare, and intelligent voice exceeded those of other fields significantly, becoming popular areas for the application of deep learning software frameworks and emerging with greater development opportunities.
Second, leading enterprises are accelerating their layout to improve the industrial ecosystem.As vertical integration becomes a consensus among global leading technology companies, tech giants represented by Google, Microsoft, and Baidu have begun to accelerate their industrial layout and vertically integrate ecosystems, leading the overall development of the industry. Currently, their strategic layout models are mainly divided into three categories: 1) Cloud service companies represented by Amazon and Microsoft continuously strengthen their intelligent service capabilities, deploying technology production tools such as deep learning software frameworks and underlying specialized hardware chips; 2) AI leading companies with significant AI technology advantages, such as Google and Baidu, deploy deep learning software frameworks based on advanced algorithms and technical strengths, and extend upwards and downwards from this core to build intelligent service ecosystems; 3) AI chip giants represented by NVIDIA are accelerating the improvement of chip performance for intelligent tasks, aiming to build an industrial ecosystem that coordinates software and hardware.
Source: Frost & Sullivan consulting
Third, the strategic position of the country as a rising power has been elevated, and the policy environment continues to be optimized.China has accelerated its layout and planning of the AI industry, having successively issued a series of important documents such as 'Made in China 2025' and the 'Three-Year Plan for the Development of New Data Centers (2021-2023)'. In the '14th Five-Year Plan for National Economic and Social Development of the People's Republic of China' promulgated in March 2021, 'deep learning frameworks' were included in the field of 'new-generation artificial intelligence', becoming a frontier innovation technology supported by the state. Currently, China has elevated artificial intelligence to a national strategy, and the intensive introduction of policies will provide fertile ground for the deep learning software framework industry, promoting related technologies to gradually move towards practical implementation.
Four typical small-scale ecological models have taken shape, shifting from a diverse array of activities to a competition among a few
Driven by core driving factors, the Chinese deep learning software framework industry has demonstrated three major trends, gradually clarifying the competitive landscape in this process.
First, original technological innovation breakthroughs are made to create an integrated innovation ecosystem.China not only possesses globally leading levels in visual, natural language processing, and speech intelligent task engineering implementation, but its original deep learning technological innovations are also in an unprecedented period of activity. According to data from the China Academy of Information and Communications Technology (CAICT), the total number of patent applications in China has reached 301,000, accounting for 39% of the global total and ranking first. As of September 2021, China had applied for a total of 909,401 patents in the field of artificial intelligence, with 253,811 patents authorized. The breakthroughs in original technological innovation in China, combined with the policy orientation of artificial intelligence, will promote the formation of an integrated innovation ecosystem that combines industry, academia, research institutions, and application. The 14th Five-Year Plan released in 2021 will guide the industry to form a community ecosystem connecting innovative entities such as enterprises, universities, research institutes, and government agencies.
Second, the rudimentary form of the ecosystem is gradually emerging, with multiple small ecosystems forming simultaneously.The embryonic global industrial ecosystem is gradually emerging. On one hand, it is manifested by leading companies represented by Google TensorFlow and Meta PyTorch constructing open-source deep learning software framework ecosystems, attempting to dominate both application interfaces and hardware adaptation. On the other hand, it is reflected in four typical small ecosystem models formed by different industry entities leveraging their respective advantages: 1) AI manufacturers represented by Amazon, Baidu, and Google are actively building AI infrastructure ecosystems; 2) AI technology service enterprises and internet companies are entering the market with their technical advantages in vision and voice, accelerating the creation of vertical industry technology service platforms and solution ecosystems; 3) Traditional enterprises are entering the market with their industry experience, actively building innovative ecosystems that encompass basic research, achievement transformation, and industrial cultivation in multiple dimensions; 4) Hardware manufacturers represented by NVIDIA and Intel are taking chip design and system integration as entry points, accelerating the construction of a software-hardware collaborative industrial ecosystem.
Source: Frost & Sullivan consulting
Thirdly, the pattern is gradually becoming clear, shifting from a proliferation of diverse offerings to a competition among a few players.Currently, the mainstream deep learning software framework landscape is gradually becoming clear, shifting from a proliferation of diverse offerings to a competition among a few key players. The focus of industry competition will shift from model libraries to usability and hardware adaptation optimization. Advanced language interfaces and hardware adaptation optimization have become crucial for building barriers in open-source frameworks. On one hand, advanced language interfaces encapsulate key functions such as model construction and training within backend frameworks, reducing the R&D threshold; on the other hand, hardware adaptation optimization aims to solve the problems of complex adaptation and uneven performance caused by various hardware compilation tools. Unifying compilation tools and languages has become a key layout point for mainstream open-source development frameworks.
Fierce competition is taking place around success factors, and the three dimensions interpret the comprehensive competitiveness of major manufacturers.
There are three key factors for winning in the Chinese deep learning software framework market: technical strength, ecological scale, and functional experience.Market demand is met through technical strength, which can be reflected as a manufacturer's product capability; hardware computing power and community education ecosystem are the underlying support, mainly manifested as an enterprise's ecological capability; functional experience determines user stickiness, which can be presented as a manufacturer's application capability.
In the process of increasingly clear competition patterns, relevant manufacturers need to overcome barriers in technology, talent, capital, and brand, and engage in fierce competition around the three key success factors. Currently, the players in the deep learning software framework industry mainly include Google TensorFlow, Meta PyTorch, Baidu PaddlePaddle, Amazon MXNet, and Huawei MindSpore. The future leader of the industry is likely to emerge from one of these three. This report will evaluate the comprehensive performance of deep learning software frameworks in terms of application, product, and ecosystem capabilities, and compare the overall competitiveness of mainstream deep learning software framework vendors.The report results show that Baidu ranks first in comprehensive competitiveness among deep learning software framework vendors, with significant advantages in application capability, technical capability, and ecosystem capability.
Source: Frost & Sullivan consultancy
