The global deep learning market was valued at USD 49.6 billion in 2022 and is expected to expand at a compound annual growth rate (CAGR) exceeding 33.5% from 2023 to 2030. The technology is gaining prominence because of advancements in data center capabilities, high computing power, and its ability to perform tasks without relying on human input. Moreover, the rapid adoption of cloud-based technology across several industries is fueling the growth of the deep learning industry.
Deep learning algorithms can perform several repetitive and routine tasks more efficiently than human beings. Additionally, it can also guarantee the quality of the work and provides additional features like key insights. Thus, implementing deep learning use within organizations can save time and money, which eventually frees up the employees to perform creative tasks that require human participation. Therefore, deep learning is considered a disruptive technology across several end-use industries, uplifting the demand for technology during the forecast period.
Deep learning technology has grown due to recent developments in neural network architecture, training algorithms, graphics processing units (GPU), and the availability of a significant amount of data across sectors. The increasing adoption of robots, IoT, cybersecurity applications, industrial automation, and machine vision technology led to a large volume of data. This data can serve as a training module in deep learning algorithms, which help diagnose and test purposes.
The deep learning algorithms learn from past experiences and create a consolidated data environment. The more data there is, the more accurate the results will be, and the data will be managed consistently. Deep learning finds its application in machine translation, chatbots, and service bots. A trained Deep Neural Network (DNN) translates a sentence or a word without using a large database. DNNs provide more accurate and better results than conventional machine translation approaches, which improves system performance.
Deep learning algorithms can be used in chatbots and service bots to improve customer services and reduce the call center burden. Deep learning platform application in chatbots involves Automatic Speech Recognition (ASR) to translate audio to text and Natural Language Processing (NLP) for the automated call transfer process. According to the survey done by Oracle Corporation in 2018, 80% of businesses are currently using chatbots.
Additionally, AI chatbots that are industry specific are expected to witness more market penetration during the forecast period. Various businesses are seeking providers offering industry-specific solutions that cater to their specific business-related challenges. For instance, during Covid-19, Haptik developed a helpdesk called MyHovCorona helpdesk chatbot for the government of India to help people with their queries about Covid-19.
The software segment led the market and accounted for a revenue share of more than 48% in 2022. The number of software tools for developers has grown significantly over the last few years. As a result, companies are developing deep learning frameworks through a high level of programming, powerful tools, and libraries that will help design, train, and validate deep neural networks. Moreover, the ONNX architecture, machine comprehension, and edge intelligence further enhance the deep learning experience across organizations.
Various startups and established companies focus on new hardware innovations to support efficient deep learning processing. Wave Computing, Inc.; Cerebras Systems Inc.; and Mythic are some of the startups working on developing deep learning chipsets and hardware. Investors and big corporate companies are also showing keen interest in these startups, accelerating the growth of deep-learning technology.
For instance, in July 2018, Xilinx, Inc. acquired DeePhi Technology Co., Ltd., a Beijing-based startup company working to develop neural networks and provide end-to-end applications on deep-learning processor unit (DPU) platforms.
The Graphics Processing Unit (GPU) held the largest market share of around 56.3% in 2022. GPUs are a widely used hardware category for improving training and classification processes in Computer Neural Networks (CNNs) as it holds high memory bandwidth and throughput. Moreover, GPU provides better computational ability allowing the system to do multiple parallel processes. Multi-GPU enhances deep learning performance by combining several GPUs in one computer.
Moreover, it offers a fast and accurate computational ability to perform a broad set of tasks concurrently in real-time. Multi-GPU helps in object detection for the autonomous car. The system needs to perform a comprehensive set of tasks in quick successions, such as detecting obstacles, determining boundary lines, and intersection detection. Several innovations are advancing deep learning. For instance, In May 2020, NEUCHIPS corporation announced the world's first deep learning recommendation model called RecAccelTM. This can perform 500,000 inferences per second.
FPGA has emerged as the best possible choice for deep learning technology. FPGA configurations were once only used for training, but they are now widely employed for various applications. FPGA is flexible, fast, power-efficient, and offers a good application for data processing in data centers. Moreover, FPGAs have gained prominence among engineers and researchers as they help to swiftly prototype several designs in significantly faster periods than a traditional IC.
Image recognition held the largest market share of around 40.7% in 2022. Deep learning can be used in stock photography and video websites to make visual content discoverable for the user. The technology can be used in visual search, allowing users to search for similar images or products using a reference image. Moreover, the technology can be used in medical image analysis, facial recognition for security and surveillance, and image detection on social media analytics.
The increasing visual content on social media and the need for content modernization will drive image recognition applications' deep learning market. For instance, in 2018, Instagram announced a new feature based on deep learning algorithms for describing photos to users with visual impairments.
The feature automatically identifies the photo using image recognition technology and then reads its automated description of the photo. Also, in March 2021, Facebook developed a deep learning solution called SEER (Self-supERvised). This solution can autonomously work its way through the dataset and can learn from any random group of unlabeled images on the internet.
The data mining application is expected to grow at the highest CAGR, over 37%, during the forecast period. Deep learning can address the challenges during data mining and extraction processes, such as fast-moving streaming data, the trustworthiness of data analysis, imbalanced input data, and highly distributed input sources. A deep learning algorithm helps in semantic indexing and tagging videos, text, and images and performs the discriminative task. Deep learning possesses the ability to execute the featured engineering to perform a complex task and provide better data representation.
In November 2019, the Securities and Exchange Board of India (SEBI) announced the plan to invest USD 70 million in information technology over the next five years, focused on implementing advanced analytical tools such as machine learning, deep learning, and big data analytics for stock market prediction, data mining, and processing of unstructured data.
The automotive end-use industry contributed around 12.83% of revenue share in 2022. The autonomous vehicle is a revolutionary technology that requires a massive amount of computation power. A Deep Neural Network (DNN) can rapidly help the autonomous vehicle perform various tasks without the need for human input.
The autonomous vehicle is expected to gain momentum in the coming years, and thus various startups and large companies are working on its development. Google Inc., Uber Technologies, Inc., and Tesla, Inc. are some prominent companies showing their capabilities in developing autonomous vehicles. As a result, in December 2019, Nvidia launched the NVIDIA DRIVE platform for autonomous vehicles.
Various investments are being made to enhance the use of deep learning to improve the features of autonomous vehicles. For instance, in January 2022, Wayve, a London-based startup, raised USD 200 million. This will help the organizations create deep learning techniques to train and develop artificial intelligence capable of complex driving situations.
The healthcare segment is expected to witness the strongest growth over the forecast period. Digital transformation in the healthcare industry is expected to continue for the next few years, providing an opportunity for innovative technologies such as AI, deep learning, and data analytics to intervene in the industry. Deep learning can be used in predictive analytics, such as early detection of diseases, identifying clinical risk and its drivers, and predicting future hospitalization.
Moreover, several government initiatives to integrate AI and deep learning in healthcare are expected to drive the market over the forecast period. Currently, NITI Aayog in India is working on implementing DNN models for the early diagnosis and detection of diabetic and cardiac risk. FDA is also working on a regulatory framework to implement AI and machine learning in the healthcare industry.
North America dominated the market with a revenue share of over 36.8% in 2022, which is attributed to increased investments in artificial intelligence and neural networks. The high adoption of image and pattern recognition in the region is expected to open new growth opportunities over the forecast period. Moreover, the region is one of the early adopters of advanced technologies, rendering organizations to adopt deep learning capabilities at a faster pace.
Furthermore, increased government support is expected to provide a positive impact on the growth of the industry in the region. The establishment of subcommittees on artificial intelligence and machine learning within the federal government is providing traction for growth.
Europe has contributed significantly to the market growth as several new measures have been taken to support the artificial intelligence sector in the region to boost growth and deliver a digital economy. This, in turn, has offered considerable growth opportunities in the deep learning space. The U.K. is underpinning the technology to grow further in the areas of autonomous vehicles, smart devices, and cyber security.
NVIDIA Corporation; Intel Corporation; IBM Corporation; Google, Inc.; and Microsoft Corporation are some of the leading companies in the market. NVIDIA Corporation dominates the market with its extensive flagship offerings, providing consistent end-user experience across various sectors. Moreover, the market has witnessed several product launches and mergers & acquisition activities in the last few years.
For instance, In October 2020, NVIDIA AI and Microsoft Azure team worked together to improve the AI-powered grammar checker in Microsoft Word. The web version of Microsoft Word can now tap into NVIDIA Triton Inference Server, ONNX Runtime, and Microsoft Azure Machine Learning to provide this smart experience.
In December 2019, Intel Corp. acquired Habana Labs Ltd., an Israel-based startup working on deep learning algorithms for data center applications strengthening the AI capability of Intel Corporation. In November 2018, Amazon Web Services announced Amazon Elastic Inference, allowing users to add elastic GPU support, reducing deep learning costs by up to 75%. Moreover, LG Electronics Inc. implemented deep learning technology in home appliances such as robot cleaners, air conditioners, washing machines, and refrigerators. Some prominent players in the global deep learning market include:
Advanced Micro Devices, Inc.
Market size value in 2023
USD 69.8 billion
Revenue forecast in 2030
USD 526.7 billion
CAGR of 33.5% from 2023 to 2030
Base year for estimation
2017 - 2021
2023 - 2030
Revenue in USD billion, CAGR from 2023 to 2030
Revenue forecast, company ranking, competitive landscape, growth factors, trends
Solution, hardware, application, end-use, region
North America; Europe; Asia Pacific; South America; Middle East & Africa
U.S.; Canada; Mexico; U.K.; Germany; China; Japan; India; Brazil
Key companies profiled
Advanced Micro Devices, Inc.; ARM Ltd.; Clarifai Inc.; Entilic; Google, Inc.; HyperVerge; IBM Corporation; Intel Corporation; Microsoft Corporation; NVIDIA Corporation
Free report customization (equivalent up to 8 analysts’ working days) with purchase. Addition or alteration to country, regional & segment scope
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This report forecasts revenue growth at the global, regional, and country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2017 to 2030. For this study, Grand View Research has segmented the global deep learning market report based on solution, hardware, application, end-use, and region:
Solution Outlook (Revenue, USD Million, 2017 - 2030)
Maintenance & support services
Hardware Outlook (Revenue, USD Million, 2017 - 2030)
Central Processing Unit (CPU)
Graphics Processing Unit (GPU)
Field Programmable Gate Array (FPGA)
Application-Specific Integration Circuit (ASIC)
Application Outlook (Revenue, USD Million, 2017 - 2030)
Video surveillance & diagnostics
End-use Outlook (Revenue, USD Million, 2017 - 2030)
Aerospace & Defense
Regional Outlook (Revenue, USD Million, 2017 - 2030)
Middle East and Africa
b. The global deep learning market size was valued at USD 49.6 billion in 2022 and is expected to reach USD 69.8 billion in 2023.
b. The global deep learning market size is expected to grow at a compound annual growth rate of 33.5% from 2023 to 2030 to reach USD 526.7 billion by 2030.
b. The software segment dominated the deep learning market with a share of 48.5% in 2022. This is attributed to the undergoing radical transformations by transitioning toward Software as a Service (SaaS) powered by deep and machine learning.
b. Some key players operating in the deep learning market include NVIDIA Corporation; Intel Corporation; Google, Inc.; Advanced Micro Devices, Inc.; IBM Corporation; and Microsoft Corporation.
b. Key factors that are driving the deep learning market growth include improvement in deep learning algorithms, a rise in big data analytics, and increasing adoption of artificial intelligence across various end-use verticals.
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