Global Convolutional Neural Networks Market Size, Share & Trends Analysis Report By Deployment Mode (On-Premise and Cloud), By Component (Hardware, Software, and Services), By Application, By Vertical, By Regional Outlook and Forecast, 2024 - 2031

Global Convolutional Neural Networks Market Size, Share & Trends Analysis Report By Deployment Mode (On-Premise and Cloud), By Component (Hardware, Software, and Services), By Application, By Vertical, By Regional Outlook and Forecast, 2024 - 2031

The Global Convolutional Neural Networks Market size is expected to reach $131.7 billion by 2031, rising at a market growth of 40.2% CAGR during the forecast period.

The North America region witnessed 36% revenue share in this market in 2023. This dominance can be attributed to leading technology companies, significant investments in research and development, and a strong emphasis on adopting advanced AI technologies across various sectors. North America, particularly the United States, has been at the forefront of machine learning and artificial intelligence innovations, resulting in a high demand for CNN applications in the healthcare, automotive, finance, and entertainment industries.

The major strategies followed by the market participants are Product Launches as the key developmental strategy to keep pace with the changing demands of end users. For instance, In September, 2024, NVIDIA Corporation Deep Learning Institute and Dartmouth College unveiled the Generative AI Teaching Kit, developed by, equips educators with advanced tools and practical resources to teach generative AI and large language models. This initiative prepares students to drive innovation in AI-related fields, addressing challenges in healthcare, science, and sustainable technologies. Moreover, In September, 2024, Intel Corporation unveiled Xeon 6 with Performance-cores (P-cores) and Gaudi 3 AI accelerators, addressing the rising demand for cost-effective AI infrastructure. Justin Hotard emphasized the need for choice in hardware and software, enabling customers to enhance performance, efficiency, and security in their data center workloads.

KBV Cardinal Matrix - Convolutional Neural Networks Market Competition Analysis

Based on the Analysis presented in the KBV Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Convolutional Neural Networks Market. In February, 2024, Microsoft Corporation unveiled Clifford-Steerable Convolutional Neural Networks (CS-CNNs), a new class of $\mathrm{E}(p, q)$-equivariant CNNs that process multivector fields on pseudo-Euclidean spaces $\mathbb{R}^{p,q}$. CS-CNNs leverage Clifford group-equivariant networks to achieve superior performance in fluid dynamics and relativistic electrodynamics forecasting compared to baseline methods. Companies such as Amazon Web Services, Inc., NVIDIA Corporation, and Samsung Electronics Co., Ltd. are some of the key innovators in Convolutional Neural Networks Market.

Market Growth Factors

The explosion of digital content—from social media platforms to e-commerce websites—has created an overwhelming volume of images and videos that require efficient processing and analysis. Organizations seek advanced recognition solutions to categorize, tag, and retrieve this content effectively. CNNs excel in visual recognition tasks, making them essential tools for managing and interpreting large datasets. Thus, increasing demand for advanced image and video recognition solutions propels the market's growth.

Additionally, Wearable devices are increasingly utilized for health monitoring, providing users with real-time data on vital signs, physical activity, and sleep patterns. CNNs facilitate the analysis of complex health data by processing images from sensors, such as those used in heart rate monitoring or blood oxygen level detection. This capability enables wearables to deliver accurate health insights, driving their adoption among health-conscious consumers. Therefore, growing popularity of wearable technology requiring efficient data analysis is driving the growth of the market.

Market Restraining Factors

However, Developing robust and effective CNN models requires extensive research and experimentation. Organizations must invest heavily in R&D to create algorithms that perform well in specific applications. This process often involves hiring specialized personnel, such as data scientists and machine learning engineers, whose salaries can be substantial. The need for ongoing innovation to stay competitive further increases these costs. In conclusion, high development and maintenance costs hamper the market's growth.

The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Product Launches and Product Expansions.

Deployment Mode Outlook

Based on deployment mode, this market is divided into on-premises and cloud. The cloud segment attained 43% revenue share in the convolutional neural networks market in 2023. This growth is primarily driven by the increasing adoption of cloud-based services, which offer scalable resources, flexibility, and cost-effectiveness for deploying CNN applications. Organizations are increasingly leveraging cloud infrastructure to handle the extensive computational requirements of CNNs, allowing them to process large datasets efficiently without significant upfront investments in hardware.

Component Outlook

Based on components, this market is divided into hardware, software, and services. In 2023, the software segment garnered 34% revenue share in the convolutional neural networks market. This dominance is driven by the increasing demand for advanced software solutions that facilitate the efficient deployment and operation of CNN models across various applications. These software solutions include frameworks, development tools, and platforms that enable CNNs to integrate seamlessly and scalable.

Application Outlook

On the basis of application, this market is segmented into image and video recognition, natural language processing (NLP), medical image analysis, autonomous vehicles, robotics and manufacturing, and others. The natural language processing (NLP) segment recorded 19% revenue share in the convolutional neural networks market in 2023. This can be attributed to the increasing adoption of NLP technologies in various industries, including customer service, healthcare, and finance, where understanding and processing human language is crucial.

Vertical Outlook

By vertical, this market is divided into healthcare, automotive, retail & e-commerce, IT & telecommunications, manufacturing, aerospace & defense, energy & utilities, and others. The automotive segment procured 18% revenue share in the convolutional neural networks market in 2023. This growth is driven by the widespread adoption of CNNs in autonomous driving systems, advanced driver-assistance systems (ADAS), and predictive maintenance. CNNs enable vehicles to recognize objects, pedestrians, and road signs, enhancing safety and driving efficiency.

Regional Outlook

Region-wise, this market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The Asia Pacific region generated 26% revenue share in the convolutional neural networks market in 2023. This growth is fuelled by the rapid adoption of AI technologies in countries like China, Japan, and India, driven by increasing investments in digital transformation and technology infrastructure. The region is witnessing significant advancements in healthcare, e-commerce, and manufacturing sectors, where CNNs are utilized for applications like image and video analysis, predictive maintenance, and natural language processing.

Market Competition and Attributes

The competition in the Convolutional Neural Networks (CNN) market is driven by smaller firms, startups, and academic institutions focused on niche applications and innovations. These players compete through specialized solutions, cost-efficiency, and adaptability in sectors like healthcare, automotive, and robotics, fostering creativity and diversified growth.

Recent Strategies Deployed in the Market

• Aug-2024: IBM Corporation the Telum Processor, featuring on-chip AI inference acceleration for real-time fraud detection in enterprise workloads. Developed over three years, this technology aims to enhance business insights in banking, finance, insurance, and trading applications, with a Telum-based system expected in early 2022.
• Jul-2024: H2O.ai, Inc. unveiled H2O-Danube3, a series of small language models H2O-Danube3-4B (trained on 6T tokens) and H2O-Danube3-500M (trained on 4T tokens). These models, optimized for performance on mobile devices, demonstrate strong metrics across various benchmarks and are available for public use under the Apache 2.0 license.
• Jun-2024: OpenAI, LLC announced the acquisition of Rockset, a real-time database startup, to enhance its AI infrastructure and boost performance across its products, including ChatGPT. Rockset specializes in real-time indexing, allowing instant data processing for quick queries, vital for AI applications. This acquisition will enable users to convert data into actionable intelligence.
• May-2024: OpenAI, LLC unveiled its flagship model, GPT-4o. This multimodal AI can process text, audio, and images, boasting a response time of 232 milliseconds for audio and 320 milliseconds on average, utilizing fillers to manage latency.
• May-2024: Samsung Electronics Co., Ltd. medical diagnostic division announced the acquisition of Sonio SAS, a cloud-based ultrasound OB-GYN reporting software. Sonio develops AI-powered ultrasound software that enhances care for women and infants. Its solutions streamline fetal examinations, enabling healthcare workers to quickly identify prenatal structures and share annotated findings with patients and professionals via QR code.

List of Key Companies Profiled

• NVIDIA Corporation
• Google LLC
• Microsoft Corporation
• IBM Corporation
• Amazon Web Services, Inc. (Amazon.com, Inc.)
• OpenAI, LLC
• Samsung Electronics Co., Ltd. (Samsung Group)
• Intel Corporation
• H2O.ai, Inc.
• Qualcomm Incorporated (Qualcomm Technologies, Inc.)

Global Convolutional Neural Networks Market Report Segmentation

By Deployment Mode

• On-Premise
• Cloud

By Component

• Hardware
• Software
• Services

By Application

• Image & Video Recognition
• Natural Language Processing (NLP)
• Medical Image Analysis
• Autonomous Vehicles
• Robotics & Manufacturing
• Other Application

By Vertical

• Healthcare
• Automotive
• Retail & E-commerce
• IT & Telecommunications
• Manufacturing
• Aerospace & Defense
• Energy & Utilities
• Other Vertical

By Geography

• North America
• US
• Canada
• Mexico
• Rest of North America
• Europe
• Germany
• UK
• France
• Russia
• Spain
• Italy
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• South Korea
• Australia
• Malaysia
• Rest of Asia Pacific
• LAMEA
• Brazil
• Argentina
• UAE
• Saudi Arabia
• South Africa
• Nigeria
• Rest of LAMEA