Neural Processor Trends and Forecast
The future of the global neural processor market looks promising with opportunities in the BFSI, healthcare, retail, defense agencies, media, and logistic markets. The global neural processor market is expected to reach an estimated $0.7 billion by 2030 with a CAGR of 18.9% from 2024 to 2030. The major drivers for this market are quick adoption of AI to lower operating costs and enhance customer services, growing industry acceptance of the machine learning technique, and rapid transition in the information industry.
• Lucintel forecasts that, within the application category, fraud detection will remain the largest segment over the forecast period.
Within this market, BFSI will remain the largest segment.
• In terms of regions, APAC is expected to witness highest growth over the forecast period due to extensive use of neural processors in a wide range of industries, such as agriculture, aviation, entertainment, and others, as well as, deep learning is becoming increasingly popular in many industries, and infrastructure development for machine learning and artificial intelligence is being invested in by all industries.
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Emerging Trends in the Neural Processor Market
Advent for Change in the Constant Neural Processor Introducing: The neural processor market is influenced by several emerging market trends that pose new challenges and opportunities in terms of the evolution of artificial intelligence and machine learning technology. Such trends have been mentioned with respect to the advancement of technology, new applications, and changes in the market structure.
• Renewed Attention to Energy Efficiency: This has led to a quest for low-power neural processors, which are considered necessary to meet the power-hungry requirements of AI processing cores. Patterns in the design of the chips are geared toward energy efficiency while preserving functionality. This is particularly important in mobile and edge computing scenarios. The procurement of energy-efficient processors can help reduce costs in running a business and contribute to environmental sustainability.
• Incorporation of AI-Specific Architectures: There is a growing use of AI-specific architectures in neural processors, such as TPUs and neuromorphic processors, which are accelerating the adoption of neural processors. Such custom architectures increase efficiency for accomplishing complex AI workloads. This development expedites and improves the execution of learned machine abilities by incorporating AI-specific characteristics.
• Movement to Edge Computing: The trend is shifting toward edge computing, where neural processors are mounted on devices at the network edge, as opposed to the conventional centralized data center. This trend helps enhance real-time data computation and decreases the time taken for data processing. Moreover, edge-mounted neural processors are beneficial in industries such as self-driving cars and IoT due to the need for rapid responses from the environment.
• Emergence of Tailor-Made and Programmable Neurons: There is an increasing demand for programmable and self-configurable neural processors that enable developers to effectively tailor the hardware to individual AI computation tasks. Such adaptability allows for a wide range of applications and increases efficiency through the use of relevant hardware to solve specific problems. Additionally, a less tedious strategy for developing new application processors or migrating older ones to meet newer performance requirements is made available by such customizations.
• Expansion of Cloud-Based AI Solutions: The demand for data center and cloud infrastructure-targeted neural processors is high due to the growth in the usage of cloud AI solutions. These processors are designed for highly efficient large-scale processing of AI and various workloads. As the use of cloud services continues to rise, there will be a further surge in demand for advanced neuron processing units that can handle greater volumes of data and processing power requirements.
In the neural processor market, new changes aimed at energy efficiency, AI-driven architecture, edge computing, intelligent and customizable processors, cloud computing, and others are changing the narrative of the industry. These changes foster progress, improve capabilities, and meet the ever-growing needs of AI applications.
Recent Developments in the Neural Processor Market
Developments in key neural processor market segments provide indicators of applicable technology development or shifts in market focus. These developments showcase improvements in power, application, and market strategy for the processors.
• Outpouring of Enhanced Neural Processors: Recently, there has been a release of enhanced neural processors designed for better and more efficient performance. For instance, AI-specific processing hardware has evolved significantly with solutions like Google TPUs and NVIDIA A100 Tensor Core GPUs, among others, that enable faster computations. Such processors are made to quickly and effectively perform machine learning tasks involving image or speech recognition and other applications.
• Increase in AI Accelerator Startups: The rise of AI accelerator startups is spurring progress in the design of neural processors. Companies such as Cerebras Systems and Graphcore Limited develop innovative connections and techniques that facilitate AI processing. This achievement provides new computing methods and helps further exploit the development of AI technologies across different sectors.
• Joint Ventures and Collaborations: The development of neural processors is on a fast track due to alliances and joint ventures being established between technology enterprises and research organizations. Collaborations, such as the one made by Intel with universities for AI research, promote creativity to ensure that advanced neural processing systems are developed within a short period.
• Inclusion of Neural Processors in Commercial Products: There is an increasing use of neural processors in smartphones and smart appliances. Firms like Apple and Qualcomm are incorporating neural processors into their products to add features such as facial recognition and voice assistants, enhancing the usability and functionality of the devices.
• Target Activity on Improved Security Features: Increasing attention is being paid to embedding enhanced cybersecurity measures into neural processors. Among these are hardware encryption and secure communication facilities that guarantee the protection of information and the safe operation of artificial intelligence technologies in response to concerns about data and information security.
Along with expected new developments in the neural processor market, such as advanced processor launches, the emergence of AI accelerator startups, strategic partnerships, integration of consumer electronics, and key development of security features, these indicators show the level of development and timing for deploying innovations. These technologies facilitate market advancement and address the changing needs of AI applications.
Strategic Growth Opportunities for Neural Processor Market
The emergence of strategic expansion avenues for neural processors is enabled by technological evolution, changing uses, and the need for more artificial intelligence solutions. Targeting these opportunities will enhance market share and create growth prospects within the industry.
• Penetration into Developing Regions: The penetration into developing regions, such as Southeast Asia and Latin America, represents a positive new growth avenue for neural processors. Expanding into these regions takes advantage of increasing technology adoption and infrastructure improvement, thereby creating a need for high-end processing across various applications.
• Creation of New Tailored AI Solutions: In designing micro-neural processors for specific market domains, like healthcare and automotive sectors, this area of software can be expanded further. Individualized solutions meet specific performance expectations, increasing demand and supporting industry-oriented solutions.
• Spending on Edge Computing Technologies: Another area for investment is edge computing technologies, which are growing rapidly due to the potential for prompt data processing and reduced delays. Neural processors dedicated to end-user edge devices support applications like self-driving cars and IoT, which raises efficiency levels and opens up new dimensions.
• Change in Processes Towards Advanced Production Techniques: The adoption of advanced production techniques, such as 3D chip stacking along with advanced lithography, would help improve processor performance and efficiency. Changes in fabrication technology will lead the market toward growth in designing and producing efficient and powerful neural processors for heterogeneous applications rather than merely entering new markets.
• Compatibility with AI Services Hosted in the Cloud: Finally, growth potential associated with neural processors integrated with cloud AI services arises from ensuring that flexible and broad AI solutions are met. The AI services market could leverage this cloud integration to handle processes and data volumes that are too large to support without the use of the cloud, as AI services are in high demand.
The implementation of development strategies in the neural processor market, such as entering foreign markets, creating AI-specific solutions, utilizing edge technologies, and providing cloud services, ensures growth in the market. Capitalizing on these opportunities will improve the organization’s position in the industry and facilitate continuous development.
Neural Processor Market Driver and Challenges
The neural processor market has several factors that influence its growth, termed drivers and challenges. Understanding these factors is crucial for successful navigation in the market.
The factors driving the neural processor market include:
• Increasing Demand for AI and Machine Learning: The escalating need for applications of AI and machine learning significantly calls for enhanced neural processors. The more industries utilize AI technologies in data analysis, automated processes, and other activities, the higher the demand for complex, multipurpose processors.
• Improvements in Semiconductor Technology: Improvements in the semiconductor field, such as smaller process nodes and better materials, enable the production of more powerful and energy-efficient neural processors. Such advancements increase processing capability and satisfy the rising demands of AI technology applications.
• Emergence of Edge Devices: Enhanced personal networks will create a demand for installing neural processors in edge devices. Edge computing brings computing physically closer to the user, enabling information processing at or near the source and increasing interest in neural processors for applications such as IoT and autonomous systems.
Challenges in the neural processor market include:
• High Costs of Research and Development: The costs associated with research, development, and manufacturing of advanced neural processors are consistently high. Such expenses can affect profitability margins and create barriers for new entrants in the market.
• Technological Progress Prospects: This poses a challenge for neural processor manufacturers due to the rapid pace of technological evolution in both AI and semiconductor • industries. Maintaining market competitiveness requires the continuous introduction of innovations and adjustments to the latest technologies, which can strain resources and income levels.
• Supply Chain Disruptions: Supply chain disruptions, such as the unavailability of semiconductor materials and components, may also impact the production of neural processors. This results in production delays, increased production costs, and an inability to meet market expectations.
The neuromorphic processor market is driven by increasing demands for AI, advances in semiconductors, edge computing, increased investments in research and development, and cloud services. Challenges include high costs, rapid technological changes, and supply chain disruptions. To navigate the market and achieve growth, it is crucial to address these drivers and challenges.
List of Neural Processor Companies
Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. With these strategies neural processor companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the neural processor companies profiled in this report include-
• Samsung Electronics
• HRL Laboratories
• Applied Brain Research
• Aspinity
• Bitbrain Technologies
• Halo Neuroscience
• General Vision
• BrainChip
• BrainCo
• Hewlett Packard Enterprise Development
Neural Processor by Segment
The study includes a forecast for the global neural processor by application, end use industry, and region.
Neural Processor Market by Application [Analysis by Value from 2018 to 2030]:
• Fraud Detection
• Hardware Diagnostics
• Financial Forecasting
• Image Optimization
• Other Applications
Neural Processor Market by End Use Industry [Analysis by Value from 2018 to 2030]:
• BFSI
• Healthcare
• Retail
• Defense Agencies
• Media
• Logistics
• Others
Neural Processor Market by Region [Analysis by Value from 2018 to 2030]:
• North America
• Europe
• Asia Pacific
• The Rest of the World
Country Wise Outlook for the Neural Processor Market
The trends in the neural processor market are shifting toward advancement due to the increasing requirements for bespoke hardware stemming from rapid progress in artificial intelligence (AI) and machine learning. With the growing reliance on artificial intelligence, especially in India, neural health processors are positioned to be at the forefront of any advancements. Key markets such as India, China, Germany, and Japan are making strides as they are eager and able to acquire these technological developments to strengthen their positions in the competitive global technology market.
• United States: In the US, the neural processor market is experiencing rapid innovation driven by major tech companies and startups. There has been a notable increase in investments aimed at developing more efficient and powerful neural processors for AI and machine learning applications. Companies like NVIDIA and Intel are pushing the boundaries with new product launches and partnerships focused on enhancing processing speed and energy efficiency. Additionally, the integration of neural processors in edge devices is gaining traction, driven by the need for real-time data processing and analysis.
• China: ChinaÄX%$%Xs neural processor market is expanding rapidly, supported by strong government initiatives to boost AI development and significant investments in semiconductor manufacturing. The country has emerged as a global leader in AI research and is heavily investing in neural processing technology for applications ranging from smart cities to autonomous vehicles. The focus on energy-efficient designs aligns with ChinaÄX%$%Xs goals for sustainable technology, positioning local companies for substantial growth in this market.
• India: In India, the neural processor market is witnessing growth due to increasing digitalization and the adoption of AI across various sectors. Government initiatives promoting AI and semiconductor manufacturing are encouraging investments from domestic and international companies. Indian startups are exploring innovative applications for neural processors, particularly in healthcare, agriculture, and smart city projects.
• Germany: Germany is focusing on developing energy-efficient neural processors to meet the demands of AI and machine learning. The market is characterized by collaborations between academia and industry to foster research and development in this field. The emphasis on automotive applications, particularly in autonomous driving, is driving innovation in neural processing technology, with local companies seeking to develop cutting-edge solutions for the automotive sector.
• Japan: JapanÄX%$%Xs neural processor market is driven by advancements in robotics and automation. The country is investing in research and development to create specialized neural processors for applications in robotics, manufacturing, and healthcare. The focus on improving the efficiency of AI algorithms and reducing power consumption aligns with JapanÄX%$%Xs commitment to technological innovation and sustainability.
In conclusion, the neural processor market is poised for significant growth across various global regions, fueled by advancements in AI, government support, and the increasing adoption of edge computing technologies. By capitalizing on emerging trends and addressing market challenges, companies can position themselves for success in this rapidly evolving landscape.
Features of the Global Neural Processor Market
Market Size Estimates: Neural processor market size estimation in terms of value ($B).
Trend and Forecast Analysis: Market trends (2018 to 2023) and forecast (2024 to 2030) by various segments and regions.
Segmentation Analysis: Neural processor market size by application, end use industry, and region in terms of value ($B).
Regional Analysis: Neural processor market breakdown by North America, Europe, Asia Pacific, and Rest of the World.
Growth Opportunities: Analysis of growth opportunities in different application, end use industry, and regions for the neural processor market.
Strategic Analysis: This includes M&A, new product development, and competitive landscape of the neural processor market.
Analysis of competitive intensity of the industry based on Porter’s Five Forces model.
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FAQ
Q1. What is the neural processor market size?
Answer: The global neural processor market is expected to reach an estimated $0.7 billion by 2030.
Q2. What is the growth forecast for neural processor market?
Answer: The global neural processor market is expected to grow with a CAGR of 18.9% from 2024 to 2030.
Q3. What are the major drivers influencing the growth of the neural processor market?
Answer: The major drivers for this market are quick adoption of AI to lower operating costs and enhance customer services, growing industry acceptance of the machine learning technique and rapid transition in the information industry.
Q4. What are the major segments for neural processor market?
Answer: The future of the neural processor market looks promising with opportunities in the BFSI, healthcare, retail, defense agencies, media, and logistic markets.
Q5. Who are the key neural processor market companies?
Answer: Some of the key neural processor companies are as follows:
• Samsung Electronics
• HRL Laboratories
• Applied Brain Research
• Aspinity
• Bitbrain Technologies
• Halo Neuroscience
• General Vision
• BrainChip
• BrainCo
• Hewlett Packard Enterprise Development
Q6. Which neural processor market segment will be the largest in future?
Answer: Lucintel forecasts that fraud detection will remain the largest segment over the forecast period.
Q7. In neural processor market, which region is expected to be the largest in next 5 years?
Answer: APAC is expected to witness highest growth over the forecast period due to extensive use of neural processors in a wide range of industries, such as agriculture, aviation, entertainment, and others, as well as, deep learning is becoming increasingly popular in many industries, and infrastructure development for machine learning and artificial intelligence is being invested in by all industries.
Q.8 Do we receive customization in this report?
Answer: Yes, Lucintel provides 10% customization without any additional cost.
This report answers following 11 key questions:
Q.1. What are some of the most promising, high-growth opportunities for the neural processor market by application (fraud detection, hardware diagnostics, financial forecasting, image optimization, and other applications ), end use industry (BFSI, healthcare, retail, defense agencies, media, logistics, and others ), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
Q.2. Which segments will grow at a faster pace and why?
Q.3. Which region will grow at a faster pace and why?
Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
Q.5. What are the business risks and competitive threats in this market?
Q.6. What are the emerging trends in this market and the reasons behind them?
Q.7. What are some of the changing demands of customers in the market?
Q.8. What are the new developments in the market? Which companies are leading these developments?
Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?
For any questions related to Neural Processor Market, Neural Processor Market Size, Neural Processor Market Growth, Neural Processor Market Analysis, Neural Processor Market Report, Neural Processor Market Share, Neural Processor Market Trends, Neural Processor Market Forecast, Neural Processor Companies, write Lucintel analyst at email: helpdesk@lucintel.com. We will be glad to get back to you soon.