Summary

Overview
Global Neural Processor Market reached US$ 224.3 Million in 2023 and is expected to reach US$ 882.7 Million by 2031, growing with a CAGR of 18.8% during the forecast period 2024-2031.
Neural processors are crucial for applications such as computer vision and autonomous systems because of their unique ability to speed up deep learning tasks like training and inference. Processing solutions that are both efficient and low latency are necessary for edge computing systems, which handle data closer to the source or endpoint devices. High-performance and energy-efficient neural processors are ideal for edge computing deployments, enabling AI inference at the network edge for applications like driverless cars, smart cities and Internet of Things devices.
Processing data at the edge of the network, including edge servers to IoT devices and sensors, is known as edge computing. Neural processors are essential for allowing AI inference and edge decision-making in real time because they offer low latency and high-performance computing capabilities. Neural processor demand is driven by the growth of edge computing applications in domains such as industrial automation, driverless cars and smart cities.
North America is dominating the market due to the growing adoption of neural processors due to the increase in the major key player's investment in the development of neural processors. The growing investment by major key players for the neural processor helps to boost regional market growth over the forecast period. For instance, on March 20, 2024, indie Semiconductor, Inc., an auto-tech company invested in Expedera Inc. The partnership will deliver customized artificial intelligence-enabled processing capabilities for sensing solutions targeting Advanced Driver Assistance Systems (ADAS) and includes a commercial agreement to integrate customized Expedera Origin NPU processing solutions into future indie products.
Dynamics
Technological Advancements
Advancements in semiconductor technology, architecture design and power management contribute to the development of energy-efficient neural processors. Reduced power consumption and optimized energy utilization make neural processors suitable for applications requiring low-power solutions, such as mobile devices, edge computing devices, IoT endpoints and battery-powered systems. Energy-efficient neural processors attract customers seeking cost-effective and environmentally friendly AI solutions.
Technological advancements enable neural processors to scale in terms of processing cores, memory capacity and computational resources. Scalable architectures allow manufacturers to offer neural processors with varying performance levels and configurations to meet diverse customer requirements. Flexibility in design and customization options further enhances market competitiveness and customer satisfaction. Intel incorporates optimizations into the AI frameworks utilized by developers and provides fundamental libraries to make uses highly performant and portable across various hardware types to make AI hardware technologies as accessible and user-friendly as feasible.
Increasing Demand for Artificial Intelligence (AI) Applications
One of the main factors propelling the market for neural processors is the spread of AI applications in a variety of industries, including healthcare, banking, automotive, retail and manufacturing. Natural language processing (NLP), forecasting, picture recognition and other advanced abilities are made possible by neural processors, which are the brains of AI algorithms, deep learning models and machine learning tasks. Neural processor demand has been driven by the exponential rise of data created from digital sources, IoT devices and other sources. The processors are essential to big data analytics and real-time data processing applications since they are designed to handle massive amounts of data and carry out intricate calculations.
Edge computing architectures are becoming increasingly common, particularly in Internet of Things deployments, where AI processing occurs closer to the data source or endpoint devices. For edge AI applications, neural processors with low power consumption and great computing power are ideally suited. It allow for real-time data processing, edge AI inference, lower latency and increased efficiency in IoT ecosystems.
Neural processor demand is fueled in part by the growth of edge AI setups. Neural processors are used by cloud service providers and AI service platforms to provide developers and businesses with AI services and solutions. Cloud-based AI applications like chatbots, sentiment analysis, recommendation engines, speech recognition, language translation and data analytics have been rendered more efficient, scalable and affordable by using neural processors.
High Development Costs
As new entrants, particularly smaller businesses or startups with limited funding, the high development costs provide obstacles to the entrance. As a result, there is less room for competition in the market, which might lead to a concentration of market share among well-established businesses as well as fewer innovations and variety in product offers. Research and development (R&D) projects aiming at developing neural processing technology are discouraged from receiving funding because of high expenditures. Delays in introducing new features or enhancements, longer cycles of innovation and a lack of product distinction might result from this.
To recover the significant development expenditures, manufacturers will have to increase the price of their neural processors. In price-sensitive market groups, this might reduce the competitiveness of the products and hinder their market penetration, especially in emerging economies or economic industries. Businesses have to give a large amount of their financial resources, human capital and time to the development of neural processors. The entire growth and competitiveness of the organization are impacted by this allocation of resources, which could take them away from other critical areas like customer service, marketing, sales and ecosystem partnerships.
Segment Analysis
The global neural processor market is segmented based on application, end-user and region.
Growing Adoption of Neural Processor in Fraud Detection
Based on the application, the neural processor market is segmented into fraud detection, hardware diagnostics, financial forecasting, image optimization and others.
As neural processors are exceptionally proficient at pattern recognition, they are very useful for recognizing trends and abnormalities that point to fraud. It examine enormous volumes of data from several sources, like network activity and financial transactions, to spot unusual trends that help to detect fraud. Real-time fraud detection capabilities are made possible by neural processors, which provide organizations the ability to identify and stop fraudulent activity as it occurs. Decisions are taken quickly and proactive fraud protection measures can implemented because of neural processors' efficiency and speed in analyzing massive datasets in actual time.
On February 01, 2024, Mastercard launched a generative AI model that helps to boost fraud detection by up to 300%. The company claims that it has built its own AI model that helps various banks detect bank fraud. Complex behavioral analysis, including anomaly identification and user behavior profiling, may be carried out via neural processors. Neural processors can detect abnormalities in user behavior that can point to fraudulent activity by examining patterns in user behavior, such as past transactions, login habits and travel pathways.
Geographical Penetration
North America is Dominating the Neural Processor Market
Research and development in artificial intelligence (AI), machine learning and semiconductor technologies focuses on North America. Leading technology companies, research centers and startups that propel advances in neural processing designs, algorithms and applications are based in the region. The semiconductor and artificial intelligence industries in the region are flourishing because of collaboration between government, business, academic institutions and venture capital companies. The ecosystem promotes the creation of neural processing solutions for a range of applications, encourages innovation and accelerates up technology transfer.
Numerous of the top semiconductor companies, producers of AI chips and global technological giants have their headquarters or a major presence in North America. The businesses such as NVIDIA, Intel, AMD, Google, Apple, Qualcomm, IBM and Apple are essential in advancing the use of neural processors in a variety of sectors. The semiconductor and AI industries receive a lot of money and investments from North America.
Competitive Landscape.
The major global players in the market include Google Inc., Intel corporation, Qualcomm Technologies, Inc., Ceva, Inc., BrainChip, Inc., NVIDIA Corporation, Graphcore, Hewlett Packard Enterprise Development LP, HRL Laboratories, LLC and Ceva, Inc.
COVID-19 Impact Analysis
COVID-19 created disruptions to globally supply chains, which affected the major key players of semiconductors. Manufacturers of neural processors encountered challenges in sourcing raw materials and logistical issues that affected the supply of neural processors to the market. In many organizations, the pandemic accelerated the digital transformation. The has increased demand for machine learning and artificial intelligence technologies, including neural processors. E-commerce and remote work all saw notable increases during the pandemic.
The use of AI-powered applications in the healthcare sector such as medical imaging analysis and patient monitoring, increased significantly during the pandemic. Large healthcare datasets were processed quickly by neural processors, which also helped to speed up research and enhance patient outcomes. Neural processors saw growing popularity in edge devices for real-time AI inference and processing with the rise of IoT devices and edge computing solutions. Neural processors that are additionally powerful and efficient are needed for edge AI applications that are becoming increasingly popular in smart cities, driverless cars, industrial automation and Internet of Things sensors.
Russia-Ukraine War Impact Analysis
The issue has the potential to disrupt semiconductor manufacturers' supply networks, especially those that manufacture neural processors. With its semiconductor manufacturing facilities, Russia and Ukraine both contribute to the world's chip production. Any interruptions to these facilities or logistical systems result in a scarcity of supplies, which would affect the global availability of neural processors.
Neural processing and artificial intelligence (AI) technology see a rise in demand for military applications as the war contributes to military operations and defense capabilities. Defense contractors and government organizations are experiencing a spike in demand for these processors since they are utilized in drones, surveillance systems, autonomous vehicles and other defense-related technology.
The conflict causes geopolitical tensions that result in trade restrictions, export controls or sanctions on the export of technology, particularly neural processors. The has an impact on the global commerce of semiconductor technology and parts, restricting market access and creating uncertainty for companies that make brain processors globally. Technology development objectives change as a result of the war, with a stronger emphasis on neural processing applications for the military and defense sectors. Research and development activities refocused on improving AI capabilities for military applications, which might affect how the neural processor industry is evolving in terms of innovation.
By Application
• Fraud Detection
• Hardware Diagnostics
• Financial Forecasting
• Image Optimization
• Other
By End-User
• BFSI
• Healthcare
• Retail
• Defense Agencies
• Logistics
• Other
By Region
• North America
o U.S.
o Canada
o Mexico
• Europe
o Germany
o UK
o France
o Italy
o Spain
o Rest of Europe
• South America
o Brazil
o Argentina
o Rest of South America
• Asia-Pacific
o China
o India
o Japan
o Australia
o Rest of Asia-Pacific
• Middle East and Africa
Key Developments
• On December 16, 2023, Intel launched Core Ultra processors for ‘AI PCs’ with a dedicated NPU. At its "AI Everywhere" introduction event, this is the first batch of consumer-segment processors with a dedicated neural processing unit (NPU), enabling on-device generative AI experience.
• On October 23, 2023, Neurxcore, launched a neural processor in the market. The product line, according to the business, utilizes NVIDIA's research and shows notable gains in energy economy, performance and feature set when compared to the original NVIDIA version.
• On April 19, 2022, Synopsys, Inc. launched Industry's Highest Performance Neural Processor IP in the market. With up to 96K MACs with improved utilization, new sparsity capabilities and a new interconnect for scalability, the DesignWare ARC NPX6 NPU IP offers industry-leading performance and power efficiency of 30 TOPS/Watt.
Why Purchase the Report?
• To visualize the global neural processor market segmentation based on application, end-user and region, as well as understand key commercial assets and players.
• Identify commercial opportunities by analyzing trends and co-development.
• Excel data sheet with numerous data points of neural processor market-level with all segments.
• PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
• Product mapping available as excel consisting of key products of all the major players.
The global neural processor market report would provide approximately 54 tables, 48 figures and 380 Pages.
Target Audience 2024
• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies

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Table of Contents

Table of Contents
1. Methodology and Scope
1.1. Research Methodology
1.2. Research Objective and Scope of the Report
2. Definition and Overview
3. Executive Summary
3.1. Snippet by Application
3.2. Snippet by End-User
3.3. Snippet by Region
4. Dynamics
4.1. Impacting Factors
4.1.1. Drivers
4.1.1.1. Technological Advancements
4.1.1.2. Increasing Demand for Artificial Intelligence (AI) Applications
4.1.2. Restraints
4.1.2.1. High Development Costs
4.1.3. Opportunity
4.1.4. Impact Analysis
5. Industry Analysis
5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion
6. COVID-19 Analysis
6.1. Analysis of COVID-19
6.1.1. Scenario Before COVID
6.1.2. Scenario During COVID
6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion
7. By Application
7.1. Introduction
7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
7.1.2. Market Attractiveness Index, By Application
7.2. Fraud Detection*
7.2.1. Introduction
7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Hardware Diagnostics
7.4. Financial Forecasting
7.5. Image Optimization
7.6. Other
8. By End-User
8.1. Introduction
8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
8.1.2. Market Attractiveness Index, By End-User
8.2. BFSI*
8.2.1. Introduction
8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Healthcare
8.4. Retail
8.5. Defense Agencies
8.6. Logistics
8.7. Other
9. By Region
9.1. Introduction
9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
9.1.2. Market Attractiveness Index, By Region
9.2. North America
9.2.1. Introduction
9.2.2. Key Region-Specific Dynamics
9.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
9.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
9.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
9.2.5.1. U.S.
9.2.5.2. Canada
9.2.5.3. Mexico
9.3. Europe
9.3.1. Introduction
9.3.2. Key Region-Specific Dynamics
9.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
9.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
9.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
9.3.5.1. Germany
9.3.5.2. UK
9.3.5.3. France
9.3.5.4. Italy
9.3.5.5. Spain
9.3.5.6. Rest of Europe
9.4. South America
9.4.1. Introduction
9.4.2. Key Region-Specific Dynamics
9.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
9.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
9.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
9.4.5.1. Brazil
9.4.5.2. Argentina
9.4.5.3. Rest of South America
9.5. Asia-Pacific
9.5.1. Introduction
9.5.2. Key Region-Specific Dynamics
9.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
9.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
9.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
9.5.5.1. China
9.5.5.2. India
9.5.5.3. Japan
9.5.5.4. Australia
9.5.5.5. Rest of Asia-Pacific
9.6. Middle East and Africa
9.6.1. Introduction
9.6.2. Key Region-Specific Dynamics
9.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
9.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
10. Competitive Landscape
10.1. Competitive Scenario
10.2. Market Positioning/Share Analysis
10.3. Mergers and Acquisitions Analysis
11. Company Profiles
11.1. Google Inc.*
11.1.1. Company Overview
11.1.2. Product Portfolio and Description
11.1.3. Financial Overview
11.1.4. Key Developments
11.2. Intel corporation
11.3. Qualcomm Technologies, Inc.
11.4. Ceva, Inc.
11.5. BrainChip, Inc.
11.6. NVIDIA Corporation
11.7. Graphcore
11.8. Hewlett Packard Enterprise Development LP
11.9. HRL Laboratories, LLC
11.10. Ceva, Inc.
LIST NOT EXHAUSTIVE
12. Appendix
12.1. About Us and Services
12.2. Contact Us