Deep Learning Market Size to Surpass USD 891.13 BN by 2032
The deep learning market size was estimated at USD 49.19 billion in 2022 and is expected to surpass around USD 891.13 billion by 2032 and poised to grow at a compound annual growth rate (CAGR) of 33.6% during the forecast period 2023 to 2032.
Key Takeaways:
Deep Learning Market Report Scope
| Report Attribute | Details |
| Market Size in 2023 | USD 65.72 Billion |
| Market Size by 2032 | USD 891.13 Billion |
| Growth Rate From 2023 to 2032 | CAGR of 33.6% |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Segments Covered | Solution, Hardware, Application, End-use, Region |
| Market Analysis (Terms Used) | Value (US$ Million/Billion) or (Volume/Units) |
| Report Coverage | Revenue forecast, company ranking, competitive landscape, growth factors, and trends |
| Key Companies Profiled | Advanced Micro Devices, Inc.; ARM Ltd.; Clarifai Inc.; Entilic; Google, Inc.; HyperVerge; IBM Corporation; Intel Corporation; Microsoft Corporation; NVIDIA Corporation |
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.
Solution Insights
The software segment led the market and accounted for a revenue share of more than 48.19% 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.
Hardware Insights
The Graphics Processing Unit (GPU) held the largest market share of around 56.13% 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.
Application Insights
Image recognition held the largest market share of around 40.17% 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.
End-Use Insights
The automotive end-use industry contributed around 12.89% 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.
Regional Insights
North America dominated the market with a revenue share of over 36.11% 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.
Some of the prominent players in the Deep Learning Market include:
Segments Covered in the Report
This report forecasts revenue growth at country levels and provides an analysis of the latest industry trends in each of the sub-segments from 2018 to 2032. For this study, Nova one advisor, Inc. has segmented the Deep Learning market.
By Solution
By Hardware
By Application
By End-use
By Region
Chapter 1 Market Segmentation and Scope
1.1 Market Segmentation and Scope
1.2 Market Definition
1.3 Information Procurement
1.3.1 Purchased Database
1.3.2 Nova One Advisor Internal Database
1.3.3 Secondary Sources & Third-Party Perspectives
1.3.4 Primary Research
1.4 Information Analysis
1.4.1 Data Analysis Models
1.5 Market Formulation & Data Visualization
1.6 Data Validation & Publishing
Chapter 2 Executive Summary & Market Snapshot
2.1 Market Outlook
2.2 Segmental Outlook
2.3 Competitive Insights
Chapter 3 Deep Learnings Industry Outlook, Trends & Scope
3.1 Market Introduction
3.2 Deep Learning-Market Size and Growth Prospects
3.3 Deep Learning -Value Chain Analysis
3.4 Deep Learning -Market Dynamics
3.4.1 MARKET DRIVER ANALYSIS
3.4.1.1 Introduction of new hardware for deep learning applications
3.4.1.2 Improvement in deep learning algorithms
3.4.1.3 Increased penetration in big data analytics
3.4.2 MARKET RESTRAINT ANALYSIS
3.4.2.1 Scalability of deep learning models
3.4.2.2 Requirement of large training datasets for recognition
3.5 Deep Learning-Penetration & Growth Prospect Mapping
3.6 Business Environment Analysis Tools
3.6.1 PEST Analysis
3.6.2 Porter's Five Force Analysis
Chapter 4 Deep Learning Application Solution Outlook
4.1 Market Size Estimates & Forecasts and Trend Analysis, 2022 - 2032
4.2 Deep Learning Market: Solution Movement Analysis
4.2.1 Hardware
4.2.1.1 Market estimates and forecast by region, 2020 - 2032
4.2.2 Software
4.2.2.1 Market estimates and forecast by region, 2020 - 2032
4.2.3 Services
4.2.3.1 Market estimates and forecast by region, 2020 - 2032
4.2.3.2 Installation Services
4.2.3.2.1 Market estimates and forecast by region, 2020 - 2032
4.2.3.3 Integration Services
4.2.3.3.1 Market estimates and forecast by region, 2020 - 2032
4.2.3.4 Maintenance & support services
4.2.3.4.1 Market estimates and forecast by region, 2020 - 2032
Chapter 5 Deep Learning Hardware Outlook
5.1 Market Size Estimates & Forecasts and Trend Analysis, 2022 - 2032
5.2 Deep Learning Market: Hardware Movement Analysis
5.2.1 CPU
5.2.1.1 Market estimates and forecast by region, 2020 - 2032
5.2.2 GPU
5.2.2.1 Market estimates and forecast by region, 2020 - 2032
5.2.3 FPGA
5.2.3.1 Market estimates and forecast by region, 2020 - 2032
5.2.4 ASIC
5.2.4.1 Market estimates and forecast by region, 2020 - 2032
Chapter 6 Deep Learning Market Application Outlook
6.1 Market Size Estimates & Forecasts and Trend Analysis, 2022 - 2032
6.2 Deep Learning Market: Application Movement Analysis
6.2.1 Image recognition
6.2.1.1 Market estimates and forecast by region, 2020 - 2032
6.2.2 Voice recognition
6.2.2.1 Market estimates and forecast by region, 2020 - 2032
6.2.3 Video Surveillance & Diagnostics
6.2.3.1 Market estimates and forecast by region, 2020 - 2032
6.2.4 Data Mining
6.2.4.1 Market estimates and forecast by region, 2020 - 2032
Chapter 7 Deep Learning Market End-Use Outlook
7.1 Market Size Estimates & Forecasts and Trend Analysis, 2022 - 2032
7.2 Deep Learning Market: End-Use Movement Analysis
7.2.1 Automotive
7.2.1.1 Market estimates and forecast by region, 2020 - 2032
7.2.2 Aerospace & Defense
7.2.2.1 Market estimates and forecast by region, 2020 - 2032
7.2.3 Healthcare
7.2.3.1 Market estimates and forecast by region, 2020 - 2032
7.2.4 Retail
7.2.4.1 Market estimates and forecast by region, 2020 - 2032
7.2.5 Others
7.2.5.1 Market estimates and forecast by region, 2020 - 2032
Chapter 8 Regional Estimates & Trend Analysis
8.1 Market Size Estimates & Forecasts and Trend Analysis, 2022 - 2032
8.2 Deep Learning Market Share by Region, 2022 & 2030
8.3 North America
8.3.1 Market estimates and forecast by solution, 2020 - 2032
8.3.2 Market estimates and forecast by hardware, 2020 - 2032
8.3.3 Market estimates and forecast by service, 2020 - 2032
8.3.4 Market estimates and forecast by application, 2020 - 2032
8.3.5 Market estimates and forecast by end-use, 2020 - 2032
8.3.6 U.S.
8.3.6.1 Market estimates and forecast by solution, 2020 - 2032
8.3.6.2 Market estimates and forecast by hardware, 2020 - 2032
8.3.6.3 Market estimates and forecast by service, 2020 - 2032
8.3.6.4 Market estimates and forecast by application, 2020 - 2032
8.3.6.5 Market estimates and forecast by end-use, 2020 - 2032
8.3.7 Canada
8.3.7.1 Market estimates and forecast by solution, 2020 - 2032
8.3.7.2 Market estimates and forecast by hardware, 2020 - 2032
8.3.7.3 Market estimates and forecast by service, 2020 - 2032
8.3.7.4 Market estimates and forecast by application, 2020 - 2032
8.3.7.5 Market estimates and forecast by end-use, 2020 - 2032
8.3.8 Mexico
8.3.8.1 Market estimates and forecast by solution, 2020 - 2032
8.3.8.2 Market estimates and forecast by hardware, 2020 - 2032
8.3.8.3 Market estimates and forecast by service, 2020 - 2032
8.3.8.4 Market estimates and forecast by application, 2020 - 2032
8.3.8.5 Market estimates and forecast by end-use, 2020 - 2032
8.4 Europe
8.4.1 Market estimates and forecast by solution, 2020 - 2032
8.4.2 Market estimates and forecast by hardware, 2020 - 2032
8.4.3 Market estimates and forecast by service, 2020 - 2032
8.4.4 Market estimates and forecast by application, 2020 - 2032
8.4.5 Market estimates and forecast by end-use, 2020 - 2032
8.4.6 Germany
8.4.6.1 Market estimates and forecast by solution, 2020 - 2032
8.4.6.2 Market estimates and forecast by hardware, 2020 - 2032
8.4.6.3 Market estimates and forecast by service, 2020 - 2032
8.4.6.4 Market estimates and forecast by application, 2020 - 2032
8.4.6.5 Market estimates and forecast by end-use, 2020 - 2032
8.4.7 U.K.
8.4.7.1 Market estimates and forecast by solution, 2020 - 2032
8.4.7.2 Market estimates and forecast by hardware, 2020 - 2032
8.4.7.3 Market estimates and forecast by service, 2020 - 2032
8.4.7.4 Market estimates and forecast by application, 2020 - 2032
8.4.7.5 Market estimates and forecast by end-use, 2020 - 2032
8.5 Asia Pacific
8.5.1 Market estimates and forecast by solution, 2020 - 2032
8.5.2 Market estimates and forecast by hardware, 2020 - 2032
8.5.3 Market estimates and forecast by service, 2020 - 2032
8.5.4 Market estimates and forecast by application, 2020 - 2032
8.5.5 Market estimates and forecast by end-use, 2020 - 2032
8.5.6 China
8.5.6.1 Market estimates and forecast by solution, 2020 - 2032
8.5.6.2 Market estimates and forecast by hardware, 2020 - 2032
8.5.6.3 Market estimates and forecast by service, 2020 - 2032
8.5.6.4 Market estimates and forecast by application, 2020 - 2032
8.5.6.5 Market estimates and forecast by end-use, 2020 - 2032
8.5.7 India
8.5.7.1 Market estimates and forecast by solution, 2020 - 2032
8.5.7.2 Market estimates and forecast by hardware, 2020 - 2032
8.5.7.3 Market estimates and forecast by service, 2020 - 2032
8.5.7.4 Market estimates and forecast by application, 2020 - 2032
8.5.7.5 Market estimates and forecast by end-use, 2020 - 2032
8.5.8 Japan
8.5.8.1 Market estimates and forecast by solution, 2020 - 2032
8.5.8.2 Market estimates and forecast by hardware, 2020 - 2032
8.5.8.3 Market estimates and forecast by service, 2020 - 2032
8.5.8.4 Market estimates and forecast by application, 2020 - 2032
8.5.8.5 Market estimates and forecast by end-use, 2020 - 2032
8.6 South America
8.6.1 Market estimates and forecast by solution, 2020 - 2032
8.6.2 Market estimates and forecast by hardware, 2020 - 2032
8.6.3 Market estimates and forecast by service, 2020 - 2032
8.6.4 Market estimates and forecast by application, 2020 - 2032
8.6.5 Market estimates and forecast by end-use, 2020 - 2032
8.6.6 Brazil
8.6.6.1 Market estimates and forecast by solution, 2020 - 2032
8.6.6.2 Market estimates and forecast by hardware, 2020 - 2032
8.6.6.3 Market estimates and forecast by service, 2020 - 2032
8.6.6.4 Market estimates and forecast by application, 2020 - 2032
8.6.6.5 Market estimates and forecast by end-use, 2020 - 2032
8.7 MEA
8.7.1 Market estimates and forecast by solution, 2020 - 2032
8.7.2 Market estimates and forecast by hardware, 2020 - 2032
8.7.3 Market estimates and forecast by service, 2020 - 2032
8.7.4 Market estimates and forecast by application, 2020 - 2032
8.7.5 Market estimates and forecast by end-use, 2020 - 2032
Chapter 9 Competitive Analysis
9.1 Key Global Players, Recent Developments & Their Impact on the Industry
9.2 Key Company Categorization (Key innovators, Market leaders, and Emerging players)
9.3 Key Company Analysis, 2022
Chapter 10 Competitive Landscape
10.1 Advanced Micro Devices, Inc.
10.1.1 Company overview
10.1.2 Financial performance
10.1.3 Product benchmarking
10.1.4 Recent developments
10.2 ARM Ltd.
10.2.1 Company Overview
10.2.2 Financial performance
10.2.3 Product benchmarking
10.2.4 Recent developments
10.3 Clarifai, Inc.
10.3.1 Company overview
10.3.2 Financial performance
10.3.3 Product benchmarking
10.3.4 Recent developments
10.4 Entilic
10.4.1 Company overview
10.4.2 Financial performance
10.4.3 Product benchmarking
10.4.4 Recent developments
10.5 Google, Inc.
10.5.1 Company overview
10.5.2 Financial performance
10.5.3 Product benchmarking
10.5.4 Recent developments
10.6 HyperVerge
10.6.1 Company overview
10.6.2 Financial performance
10.6.3 Product benchmarking
10.6.4 Recent developments
10.7 IBM Corporation
10.7.1 Company overview
10.7.2 Financial performance
10.7.3 Product benchmarking
10.7.4 Recent developments
10.8 Intel Corporation
10.8.1 Company overview
10.8.2 Financial performance
10.8.2 Product benchmarking
10.8.3 Recent developments
10.9 Microsoft Corporation
10.9.1 Company overview
10.9.2 Financial performance
10.9.3 Product benchmarking
10.9.4 Recent developments
10.10 NVIDIA Corporation
10.10.1 Company overview
10.10.2 Financial performance
10.10.3 Product benchmarking
10.10.4 Recent developments
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