Neuromorphic Computing Market Size & Share 2024 - 2032
Market Size by Component (Hardware, Software, Services), by Deployment (Edge, Cloud), by Application (Image recognition, Signal recognition, Data mining) by End Use Industry & Forecast.
Report ID: GMI9155
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Published Date: April 2024
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Report Format: PDF
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Authors:
Suraj Gujar, Sandeep Ugale
Neuromorphic Computing Market Size
Neuromorphic Computing Market was valued at over USD 5 billion in 2023 and is expected to register a CAGR of over 25.5% between 2024 and 2032. The capacity to conduct large-scale neural network simulations makes scalability a key growth driver for the neuromorphic computing sector.
Neuromorphic Computing Market Key Takeaways
Market Size & Growth
Key Market Drivers
Challenges
Scalable neuromorphic systems provide the flexibility to increase computational capacity without losing efficiency as demand rises for processing massive volumes of data in AI and machine learning applications. Neuromorphic computing is an appealing option for sectors requiring sophisticated, high-performance computing capabilities because of its scalability, which guarantees adaptation to changing computational needs. In September 2022, Intel Corporation collaborated with the Italian Institute of Technology and the Technical University of Munich to introduce a new neural network-oriented object learning method. This partnership aims to use neuromorphic computing through an interactive online object-learning approach to enable robots to learn new objects instance with better speed and accuracy after deployment.
The need for effective and scalable substitutes for traditional computer architectures is the driving force behind the increasing demand for brain-inspired computing solutions. The growing dependence of industries on artificial intelligence and machine learning applications has made it apparent that systems that emulate the brain's energy efficiency and parallel processing capacity are essential. As businesses look for cutting-edge solutions for challenging real-time data processing problems and complicated computational activities, neuromorphic computing is expected to expand in popularity as it provides viable paths for meeting these needs.
A major obstacle in the market is the complexity of designing and programming neuromorphic devices. Neuromorphic computer designs imitate the complex neural networks seen in the brain, in contrast to standard computing structures, which use organized algorithms. Some prerequisites include hardware engineering, computer science, and neuroscience expertise. It is challenging to design effective algorithms and translate them onto hardware, which lengthens development cycles and raises costs. This intricacy may prevent widespread acceptance and restrict the market's potential for expansion.
Neuromorphic Computing Market Trends
The market for neuromorphic computing is expanding quickly as companies look for machine learning and AI solutions that are more effective. Neuromorphic systems provide increased processing power and energy efficiency by modeling the structure of the brain. In order to address the computational demands of complicated tasks while maximizing energy usage, neuromorphic computing presents a possible solution. The demand for advanced AI applications is expanding across industries, including healthcare, finance, and automotive.
By integrating neuromorphic computing with edge computing, data processing capabilities in real-time are brought to the network's edge, negating the need for data transmission to centralized servers. To reduce latency and enable faster response times for important applications like autonomous vehicles, industrial automation, and augmented reality, calculations are carried out closer to data sources such as IoT devices or sensors.
Neuromorphic Computing Market Analysis
Based on component, the market is divided into hardware, software, and services. The hardware segment is expected to reach over USD 23.5 billion by 2032.
Based on deployment, the market is segmented into edge and cloud. The edge segment is expected to register a CAGR of over 31% over the forecast period.
North America dominated the global market in 2023 with over 30% of the total revenue share. The neuromorphic computing market is expanding in North America because of the region's strong ecosystem of tech firms, top research universities, and significant investments in the semiconductor and artificial intelligence sectors. The area also gains from a highly trained labor pool, conducive regulatory frameworks, and robust government backing for R&D projects. All of these elements work together to make the region a leader in neuromorphic computing technology adoption and innovation, which supports the industry's expansion in North America.
Neuromorphic Computing Market Share
Intel Corporation and IBM Corporation held a significant share of over 15% in the neuromorphic computing industry in 2023. Intel Corporation is a leading provider of neuromorphic computing solutions, leveraging its expertise in semiconductor technologies. The company offers neuromorphic chips and platforms tailored for AI and machine learning applications. Intel's products enable efficient processing of complex data with low power consumption, driving advancements in areas such as edge computing, autonomous systems, and pattern recognition, thus shaping the future of computing.
IBM Corporation, a leading player in neuromorphic computing, offers a range of solutions leveraging its expertise in AI and semiconductor technologies. Their offerings include neuromorphic hardware development, software frameworks for neural network simulations, and consulting services for integrating neuromorphic systems into various applications. IBM aims to advance the field with innovative solutions tailored to meet diverse industry needs.
Neuromorphic Computing Market Companies
Major players operating in the industry are:
Neuromorphic Computing Industry News
The neuromorphic computing market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue (USD Billion) from 2018 to 2032, for the following segments:
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Market, By Component
Market, By Deployment
Market, By Application
Market, By End-use Industry
The above information is provided for the following regions and countries:
Research methodology, data sources & validation process
This report draws on a structured research process built around direct industry conversations, proprietary modelling, and rigorous cross-validation and not just desk research.
Our 6-step research process
1. Research design & analyst oversight
At GMI, our research methodology is built on a foundation of human expertise, rigorous validation, and complete transparency. Every insight, trend analysis, and forecast in our reports is developed by experienced analysts who understand the nuances of your market.
Our approach integrates extensive primary research through direct engagement with industry participants and experts, complemented by comprehensive secondary research from verified global sources. We apply quantified impact analysis to deliver dependable forecasts, while maintaining complete traceability from original data sources to final insights.
2. Primary research
Primary research forms the backbone of our methodology, contributing nearly 80% to overall insights. It involves direct engagement with industry participants to ensure accuracy and depth in analysis. Our structured interview program covers regional and global markets, with inputs from C-suite executives, directors, and subject matter experts. These interactions provide strategic, operational, and technical perspectives, enabling well-rounded insights and reliable market forecasts.
3. Data mining & market analysis
Data mining is a key part of our research process, contributing nearly 20% to the overall methodology. It involves analysing market structure, identifying industry trends, and assessing macroeconomic factors through revenue share analysis of major players. Relevant data is collected from both paid and unpaid sources to build a reliable database. This information is then integrated to support primary research and market sizing, with validation from key stakeholders such as distributors, manufacturers, and associations.
4. Market sizing
Our market sizing is built on a bottom-up approach, starting with company revenue data gathered directly through primary interviews, alongside production volume figures from manufacturers and installation or deployment statistics. These inputs are then pieced together across regional markets to arrive at a global estimate that stays grounded in actual industry activity.
5. Forecast model & key assumptions
Every forecast includes explicit documentation of:
✓ Key growth drivers and their assumed impact
✓ Restraining factors and mitigation scenarios
✓ Regulatory assumptions and policy change risk
✓ Technology adoption curve parameter
✓ Macroeconomic assumptions (GDP growth, inflation, currency)
✓ Competitive dynamics and market entry/exit expectations
6. Validation & quality assurance
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Our triple-layer validation process ensures maximum data reliability:
✓ Statistical Validation
✓ Expert Validation
✓ Market Reality Check
Trust & credibility
Verified data sources
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Industry databases
Proprietary and third-party market databases
Regulatory filings
Government procurement records and policy documents
Academic research
University studies and specialist institution reports
Company reports
Annual reports, investor presentations, and filings
Expert interviews
C-suite, procurement leads, and technical specialists
GMI archive
13,000+ published studies across 30+ industry verticals
Trade data
Import/export volumes, HS codes, and customs records
Parameters studied & evaluated
Every data point in this report is validated through primary interviews, true bottom-up modelling, and rigorous cross-checks. Read about our research process →