GVR Report cover Deep Learning Market Size, Share & Trends Report

Deep Learning Market Size, Share & Trends Analysis Report By Solution, By Hardware (CPU, GPU, FPGA, ASIC), By Application (Image Recognition, Voice Recognition), By End Use, By Region, And Segment Forecasts, 2022 - 2030

  • Report ID: GVR-1-68038-466-6
  • Number of Pages: 118
  • Format: Electronic (PDF)
  • Historical Range: 2017 - 2020
  • Industry: Technology

Report Overview

The global deep learning market size was valued at USD 34.8 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) exceeding 34.3% from 2022 to 2030. The technology is gaining prominence because of advancements in data center capabilities, high computing power, and the ability to perform tasks without human interactions. Moreover, the rapid adoption of cloud-based technology across several industries is fueling the growth of the market. Deep learning algorithms can perform several repetitive and routine tasks more efficiently within a shorter time than human beings. In addition, the quality of the work is maintained and provides accurate insights. Thus, implementing deep learning in the organization can save time and money, which eventually frees up the employees to perform creative tasks that need human involvement. Therefore, deep learning is considered a disruptive technology across several end-use industries, uplifting the demand for technology.

U.S. deep learning market, by solution, 2020 - 2030 (USD Billion)

Deep learning technology has grown due to recent developments in neural network architecture and 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 applications in machine translation, chatbots, and service bots. A trained Deep Neural Network (DNN) translates the 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, in 2018 there were 300,000 active chatbots on Facebook Messenger, and the number for the same is expected to increase in the coming years. 

Solution Insights

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. As a result, the 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.

Hardware Insights

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. 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.

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.  

Application Insights

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). 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 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.

End-use Insights

The automotive segment contributed around 12.2% revenue share in 2021. The autonomous vehicle is 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. Google Inc.; Uber Technologies, Inc.; and Tesla, Inc. are some prominent companies showing their capabilities in developing autonomous vehicles. For instance, in December 2019, Nvidia launched the NVIDIA DRIVE platform for autonomous vehicles.

Various investments are being made to enhance the use of deep learning in improving the features of the autonomous vehicle. 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.

Global deep learning market, by end use, 2021 (%)

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.

Regional Insights

North America dominated the market with a revenue share of over 38.6% in 2021, 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 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 the 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.

Key Companies & Market Share Insights

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 the various sectors. The market has witnessed several product launches and merger & 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.

  • ARM Ltd.

  • Clarifai, Inc.

  • Entilic

  • Google, Inc.

  • HyperVerge

  • IBM Corporation

  • Intel Corporation

  • Microsoft Corporation

  • NVIDIA Corporation

Deep Learning Market Report Scope

Report Attribute

Details

Market size value in 2022

USD 49.6 billion

Revenue forecast in 2030

USD 526.7 billion

Growth rate

CAGR of 34.3% from 2022 to 2030

Base year for estimation

2021

Historical Data

2017 - 2020

Forecast period

2022 - 2030

Quantitative units

Revenue in USD billion and CAGR from 2022 to 2030

Report coverage

Revenue forecast, company share, competitive landscape, key company categorization, growth factors, and trends

Segments covered

Solution, hardware, application, end use, region

Regional scope

North America; Europe; Asia Pacific; South America; MEA

Country scope

U.S.; Canada; Mexico; U.K.; Germany; China; India; Japan; Brazil

Key Companies Profiled

Advanced Micro Devices, Inc.; ARM Ltd.; Clarifai  Inc.; Entilic; Google, Inc.; HyperVerge; IBM Corporation; Intel Corporation; Microsoft Corporation; NVIDIA Corporation

Customization scope

Free report customization (equivalent to up to 8 analysts working days) with purchase. Addition or alteration to country, regional & segment scope.

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Segments Covered in the Report

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:

  • Deep Learning Solution Outlook (Revenue, USD Million, 2017 - 2030)

    • Hardware

    • Software

    • Service

      • Installation Services

      • Integration Services

      • Maintenance & Support Services

  • Deep Learning 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)

  • Deep Learning Application Outlook (Revenue, USD Million, 2017 - 2030)

    • Image Recognition

    • Voice Recognition

    • Video Surveillance & Diagnostics

    • Data Mining

  • Deep Learning End-use Outlook (Revenue, USD Million, 2017 - 2030)

    • Automotive

    • Aerospace & Defense

    • Healthcare

    • Manufacturing

    • Others

  • Deep Learning Regional Outlook (Revenue, USD Million, 2017 - 2030)

    • North America

      • U.S.

      • Canada

      • Mexico

    • Europe

      • Germany

      • U.K.

    • Asia Pacific

      • China

      • India

      • Japan

    • South America

      • Brazil

    • Middle East and Africa

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