Neural Network Software Market Size, Share & Trends Analysis Report By Component (Neural Network Software, Services, Platform and Other Enabling Services), By Type (Data Mining and Archiving, Optimization Software, Analytical Software, Visualization Software), By Industry (BFSI, Government and Defense, Energy and Utilities, Healthcare, Industrial Manufacturing, Media, Telecom and IT, Transportation and Logistics, Retail and e-commerce, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2024-2032

Market Overview

The global neural network software market was valued at USD 23.10 billion in 2023. It is estimated to reach USD 311.13 billion by 2032, growing at a CAGR of 33.5% during the forecast period (2024–2032). In recent years, there has been a rise in the amount of spatial data generated, which needs neural network software to process, analyze, and visualize the data efficiently, ultimately boosting market expansion. Neural network software is also used in predictive solutions to estimate future outcomes based on past instances, augmenting market expansion. Moreover, the growing adoption of cloud-based neural network software, which offers better benefits, is anticipated to create opportunities for market growth.

Neural network software encompasses software tools and frameworks designed to streamline the process of developing, training, and deploying neural networks, which are a crucial element of deep learning. Machine learning models called the composition and operation of the human brain-inspired neural networks. Neural network software has many applications, including data mining, optimization, analytics, visualization, and machine learning. Neural network software can be categorized into several sorts based on functionality and purpose, including data mining and archiving, optimization software, analytical software, and visualization software.

Highlights

• Neural network software dominates the component segment
• Analytical software dominates the type segment
• BFSI dominates the industry segment
• North America is estimated to be the largest shareholder in the market

Market Dynamics

Global neural network software market drivers

Increasing amount of spatial data

Spatial data refers to data linked to a specific position or geographical region. Spatial data can be derived from diverse sources, including satellites, drones, sensors, cameras, and mobile devices. Spatial data offers significant insights across multiple areas, including urban planning, environmental monitoring, disaster management, transportation, and agriculture. However, spatial data exhibits complexity, heterogeneity, and high dimensionality, presenting difficulties for conventional data analysis techniques.

Neural network software utilizes deep learning and artificial intelligence to process, analyze, and visualize spatial data rapidly. Neural network software can assist in extracting characteristics, recognizing patterns, and making predictions based on spatial data. This can improve the decision-making and problem-solving abilities of the users. Therefore, the rising amount of spatial data is boosting the market expansion.

Rising demand for predictive solutions

Predictive solutions encompass the ability to anticipate forthcoming outcomes or events by analyzing historical and current data. Predictive solutions can enhance the performance, productivity, and profitability of many enterprises and organizations by helping them to foresee and prepare for forthcoming circumstances. Neural network software facilitates the creation and implementation of prediction solutions by utilizing sophisticated algorithms and techniques, including regression, classification, clustering, and reinforcement learning.

Neural network software can effectively manage extensive and intricate datasets, offering precise and dependable predictions that enhance the quality and reliability of predictive solutions. Moreover, researchers are continuously endeavoring to investigate this domain further. For instance, in 2023, a group of computer scientists from New York University developed a neural network capable of explaining its forecasts. This work uncovers the factors contributing to the effectiveness of neural networks, which are the driving force behind artificial intelligence and machine learning. As a result, it sheds light on a process that has mostly been hidden from consumers. Therefore, these factors contribute to the expansion of the global market.

Global neural network software market restraint

Lack of global expertise and slow adaptation to operational challenges 

Neural network software is a nascent and burgeoning technology that needs a profound understanding and expertise to utilize and deploy effectively. However, there is a need for more professional and experienced professionals capable of designing, developing, and maintaining neural network software, particularly in developing and undeveloped areas.

In addition, neural network software encounters operational obstacles, including data security, privacy, ethics, and explainability, which might impede the implementation and approval of the technology by end-users. Hence, the absence of worldwide proficiency and sluggish adjustment to operational obstacles can impede the expansion of the neural network software market.

Global neural network software market opportunities

Increasing adoption of cloud-based neural network software

Cloud-based neural network software refers to a specific form of software accessed and utilized through the Internet instead of being installed and managed on individual computers. Cloud-based neural network software provides numerous advantages, including scalability, flexibility, accessibility, cost-effectiveness, and performance, compared to traditional on-premise neural network software.

Cloud-based neural network software allows end-users to conveniently access and operate the software from any location and at any time, eliminating the requirement for hardware and software setup and maintenance. Through encryption and authentication approaches, cloud-based neural network software can offer improved security and privacy functionalities. Moreover, numerous neural network software, such as PyTorch, may be seamlessly integrated with leading cloud platforms, including AWS, GCP, Alibaba Cloud, and Azure. Hence, the growing use of cloud-based neural network software has the potential to generate lucrative opportunities for the neural network software market.

Regional Analysis

North america dominates the global market

Based on region, the global neural network software market is bifurcated into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa.

North America is the most significant global neural network software market shareholder and is expected to expand substantially during the forecast period. North America is projected to maintain a lead in the market due to its superior technological advancements and high innovation development. Google, IBM, Microsoft, and Intel are prominent participants in North America's neural network software market. Moreover, the region is anticipated to experience substantial expansion in the neural network software market throughout the projected timeframe, owing to major technological companies' extensive implementation of neural network technology. For instance, in August 2019, Intel introduced two new processors that utilize Artificial Intelligence technologies specifically designed for large-scale computing centers. The chips, known as the Nervana Neural Network Processor (NPP) series, consist of two models, i.e., the Nervana NNPT (codenamed Spring Crest), designed for training Artificial Intelligence systems, and the Nervana NNP-I (codenamed Spring Hill), designed for handling inference. These advancements are expected to drive the regional market expansion.

Furthermore, researchers from several regional institutes have observed an increase in the development of novel technologies that utilize neural networks to improve efficiency. For instance, in April 2023, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) developed a method for flight navigation agents to excel at vision-based fly-to-target tasks in complex and unfamiliar environments. The flexible capabilities of organic brains inspired this method. The liquid neural networks demonstrated an exceptional ability to make accurate decisions in unfamiliar domains such as woods, urban landscapes, and environments with additional noise, rotation, and occlusion. These networks can adapt to new data inputs continually. These versatile models, which surpassed numerous cutting-edge counterparts in navigation tasks, have the potential to facilitate practical drone applications such as search and rescue, deliveries, and wildlife monitoring. This is expected to enhance the growth of the neural network software market.

Europe is projected to have a substantial market share due to its substantial investments in neural network software research and development. It also actively supports different programs and projects, including the European Union's Horizon 2020 program and the Human Brain Project. The market growth is expected to be influenced by integrating emerging technology into the autopilot system as part of the aviation industry's embrace of innovations. For instance, in August 2019, Daedalean disclosed that the company had secured USD 12 million in investment to advance the development of its autopilot system, which incorporates deep convolutional feed-forward neural networks, an advanced kind of Artificial Intelligence (AI). Consequently, the European region is experiencing increased demand for neural network software, contributing to market expansion.

Segmental Analysis

The global neural network software market is segmented by component, type, and industry.

Based on components, the global neural network software market is bifurcated into neural network software, services, platforms, and other enabling services. 

The neural network software segment held the largest market share. Neural network software usually appears as frameworks or libraries offering pre-designed functions and tools for constructing, training, and implementing neural networks. These tools offer a platform for researchers and developers to create and execute neural network structures. The APIs provided allow users to define layers, provide parameters, and manage the topology of the neural network. Moreover, neural network software comprises algorithms and functions that facilitate the training process, during which the model fine-tunes its parameters to reduce the disparity between expected and actual outputs. Commonly employed neural network software frameworks encompass TensorFlow, PyTorch, Keras, MXNet, and Caffe.

Based on type, the global neural network software market is segmented into data mining and archiving, analytical software, optimization software, and visualization software. 

The analytical software segment dominates the global market. Analytical software is utilized to conduct statistical and mathematical data analysis, encompassing regression, classification, clustering, and dimensionality reduction techniques. The analytics software market is expected to experience a surge in demand due to the growing popularity of artificial intelligence in trading systems. For instance, in September 2019, Deep Nexus Inc. introduced Al-powered predictive analytics specifically designed for financial markets. The company employs deep-learning neural networks and time-series data to engage in trading activities across equities, futures, commodities, and foreign exchange markets. Hence, advancements in trading systems facilitated by Artificial Intelligence (AI) technology are anticipated to propel the expansion of the analytics software segment in the neural network software market.

Based on industry, the global neural network software market is divided into BFSI, government and defense, energy and utilities, healthcare, industrial manufacturing, media, telecom and IT, transportation and logistics, retail and e-commerce, and others. 

The BFSI segment owns the highest market share. This is due to its extensive use of neural network software for various applications, including fraud detection, credit scoring, customer segmentation, and risk management. Integrating Artificial Intelligence (AI) technology in the BFS industry simplifies financial operations by providing effective procedural systems. For instance, in September 2019, Yes Bank collaborated with Microsoft to enhance the capabilities of its Artificial Intelligence-powered chatbot. The Yes Robot utilizes the Language Understanding Intelligent Service (LUIS) and other cognitive services to comprehend and address the changing banking needs of consumers without the need for human involvement. Therefore, integrating neural network technology into the banking sector enhances the operational efficiency of the BFSI segment in the neural network software market.

Recent Developments

• January 2024- Tesla started the distribution of its FSD Beta v12 upgrade to customers, representing a crucial opportunity for the company to fulfill its commitments to autonomous driving. FSD Beta v12 enhances driving capabilities in urban areas by implementing a unified neural network trained on a large dataset of video clips. This neural network replaces a substantial amount of explicit C++ code, totaling over 300,000 lines.
• November 2023- Broadcom Inc. introduced a new on-chip neural-network inference engine called NetGNT (Networking General-purpose Neural-network Traffic-analyzer) in its latest Trident 5-X12 processor, which can be programmed using the software.