Deep Learning Industry Overview
The global deep learning market size is expected to reach USD 526.7 billion by 2030, registering a CAGR of 34.3% over the forecast period, according to a new report by Grand View Research, Inc. Deep learning is expected to gain sustainable momentum in the coming years owing to its high computational ability and improved complex data-driven applications. The growing emphasis on big data analytics and Artificial Intelligence (AI) adoption in customer-centric services is expected to propel the growth of the market over the forecast period.
AI has evolved rapidly over the period, enabling the machine to perform cognitive tasks effectively. The adoption of AI across various sectors has unlocked the potential opportunities for machine learning and deep learning applications. AI-as-a-service has allowed smaller organizations to implement the AI algorithms required for deep learning applications without a large capital investment. Moreover, the availability of a large amount of data and the need for high computing power encourage Small & Medium Enterprises (SMEs) and large enterprises to invest significantly in deep learning technology.
Deep Learning Market Segmentation
Grand View Research has segmented the global deep learning market report based on solution, hardware, application, end use, and region:
Based on the Solution Insights, the market is segmented into hardware, software and service
The software segment led the deep learning market and accounted for a revenue share of more than 49.0% in 2021. The number of software tools for developers has grown significantly over the last few years.
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.
Based on the Hardware Insights, the market is segmented into CPU, GPU, FPGA and ASIC
The Graphics Processing Unit (GPU) segment held the largest market share of around 57.3% in 2021. GPUs are the widely used hardware for improving training and classification processes in Computer Neural Networks (CNNs) as it holds high memory bandwidth and throughput.
GPU provides better computational ability allowing the system to do multiple parallel processes. Multi-GPU enhances the 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 the boundary lines, and intersection detection.
Field Programmable Gate Array (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.
Based on the Application Insights, the market is segmented into image recognition, voice recognition, video surveillance & diagnostics and data mining
Image recognition held the largest market share of around 41.5% in 2021. 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 the application of image recognition. For instance, in 2018, Instagram announced a new feature based on deep learning algorithms for describing photos 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).
The data mining application segment is expected to expand at the highest CAGR of over 38.1% 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.
Based on the End-use Insights, the market is segmented into automotive, aerospace & defense, healthcare, manufacturing and others
The automotive segment contributed around 12.2% revenue share in 2021. The autonomous vehicleis a revolutionary technology that requires a massive amount of computation power.
A DNN can rapidly help the autonomous vehicle perform various tasks without human interference.
Autonomous vehicles are expected to gain momentum in the forthcoming years, and thus various startups and large companies are working on their development.
Various investments are being made to enhance the use of deep learning in improving the features of the autonomous vehicle.
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.
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.
Deep Learning Regional Outlook
North America
Europe
Asia Pacific
Asia Pacific America
Middle East & Africa (MEA)
Key Companies Profile & Market Share Insights
NVIDIA Corporation dominates the market with its extensive flagship offerings, providing consistent end-user experience across the various sectors. The market has witnessed several product launches and merger & acquisition activities in the last few years.
Some prominent players in the global deep learning market include:
Advanced Micro Devices, Inc.
ARM Ltd.
Clarifai, Inc.
Entilic
Google, Inc.
HyperVerge
IBM Corporation
Intel Corporation
Microsoft Corporation
NVIDIA Corporation
Market Industry Development
October 2020: NVIDIA AI and Microsoft Azure team worked together to improve the AI-powered grammar checker in Microsoft Word.
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.
November 2018: Amazon Web Services announced Amazon Elastic Inference, allowing users to add elastic GPU support, reducing deep learning costs by up to 75%.
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About Grand View Research
Grand View Research, U.S.-based market research and consulting company, provides syndicated as well as customized research reports and consulting services. Registered in California and headquartered in San Francisco, the company comprises over 425 analysts and consultants, adding more than 1200 market research reports to its vast database each year. These reports offer in-depth analysis on 46 industries across 25 major countries worldwide. With the help of an interactive market intelligence platform, Grand View Research Helps Fortune 500 companies and renowned academic institutes understand the global and regional business environment and gauge the opportunities that lie ahead.