Unlike standard computing models that depend on von Neumann architectures, neuromorphic computing processes information in a biologically inspired manner utilizing neural networks, analog circuits, and specialized hardware, which is mainly beneficial in applications that need pattern recognition, sensory processing, and real-time decision-making.
Neuromorphic computing market advancements are progressing rapidly, with major players investing in brain-inspired systems that mimic brain's neural networks to facilitate real-time processing capabilities and machine learning capabilities. Furthermore, researchers and tech giants are cooperating on refining neuromorphic chips with improved computational efficiency and power usage to fuel demand across different industries.
Recent advancements have focused on the incorporation of neuromorphic computing into autonomous systems, robotics and AI-driven applications. Neuromorphic solutions allow companies to simulate cognitive functions more naturally - sparking immense interest across sectors like
healthcare, automotive and robotics. Businesses are exploring neuromorphic solutions for decision making processes as well as more adaptive AI systems.
As demand for energy-efficient computing increases, neuromorphic computing presents an attractive solution. Neuromorphic chips consume significantly less power than traditional computing architectures and are ideal for edge devices. As society strives towards sustainability goals, neuromorphic computing offers an ingenious way of cutting back energy consumption when processing data or AI applications.
Neuromorphic computing offers enormous promise to transform industries such as healthcare, which relies heavily on real-time data processing and AI diagnostics for real-time patient monitoring and care delivery. Furthermore, automotive companies are exploring this emerging technology to power safer autonomous vehicles; as its application advances further investment will increase in R&D partnerships and commercialization will fuel its expansion further still.
As per intel.com Neuromorphic computing adoption has surged, with nearly 55% of technology companies integrating neuromorphic processors into their AI and machine learning systems. Around 61% of research institutes worldwide are exploring neuromorphic systems for developing energy-efficient computational models. The demand is especially high in industries like autonomous vehicles, where over 42% of companies are using neuromorphic technologies for real-time data processing.
The US Neuromorphic Computing Market
The US Neuromorphic Computing Market is projected to reach USD 2.3 billion in 2024 at a compound annual growth rate of 24.7% over its forecast period.
The strong growth opportunities in the US has neuromorphic computing market due to development in AI, Neuromorphic Computing, and robotics. Key areas like military, healthcare, and AI-driven speech recognition also drive the demand. Government funding, research initiatives, and collaboration between tech companies and academia further assists in innovation and commercialization in neuromorphic computing applications.
Further the market here is driven by advancements in AI, autonomous systems, and higher government funding for research. However, a key restraint is the high development costs and complexity of neuromorphic hardware, along with the lack of standardized frameworks, which makes adoption and integration into existing systems more challenging.
Key Takeaways
- Market Growth: The Neuromorphic Computing Market size is expected to grow by 47.3 billion, at a CAGR of 26.4% during the forecasted period of 2025 to 2033.
- By Offering: The hardware segment is expected to lead in 2024 with a majority & is anticipated to dominate throughout the forecasted period.
- By Application: The image processing segment is expected to be leading the market in 2024
- By End User: The consumer electronics segment is expected to get the largest revenue share in 2024 in the Neuromorphic Computing Market.
- Regional Insight: North America is expected to hold a 38.5% share of revenue in the Global Neuromorphic Computing Market in 2024.
- Use Cases: Some of the use cases of Neuromorphic Computing include sensory data processing, BMIs, and more.
Use Cases
- Robotics and Autonomous Systems: Neuromorphic chips can power robots with live processing and less power consumption for tasks such as navigation, object recognition, and decision-making, improving efficiency in dynamic environments.
- Edge AI and IoT Devices: With their minimal energy consumption and high processing capabilities, neuromorphic systems are ideal for Internet of Things (IoT) devices, allowing local AI processing without the need for constant cloud connectivity.
- Sensory Data Processing: Neuromorphic hardware can easily process sensory data (such as vision, sound, and touch) in real-time, making it useful in applications like prosthetics, healthcare wearables, and smart environments.
- Brain-Machine Interfaces (BMIs): Neuromorphic systems, which simulate the brain's architecture, are suitable for developing brain-machine interfaces, enabling direct communication between the brain & computers for medical applications like controlling prosthetics or restoring sensory functions.
Market Dynamic
Driving Factors
Demand for Energy-Efficient AI SolutionWith the growth of AI applications in devices such as smartphones, Neuromorphic Computing, and smart IoT gadgets, there’s an increase in demand for processors that can handle complex tasks with low power consumption, which neuromorphic computing excels at.
Advancements in Edge Computing
The expansion for real-time data processing at the edge (closer to where data is generated) drives the need for neuromorphic chips, as they provide faster, more efficient computation for AI tasks without depending on cloud infrastructure.
Restraints
High Development Costs
The research, design, and manufacturing of neuromorphic chips are very costly and resource-intensive, impacting the number of companies and research institutions that can invest in developing this technology.
Lack of Standardization
The neuromorphic computing field is still in its growing stages, and there is no standardized architecture or programming framework. It makes it challenging for developers to adopt and incorporate the technology into existing systems and slows down widespread commercialization.
Opportunities
Healthcare InnovationsNeuromorphic computing has major potential in healthcare, mainly for brain-machine interfaces, neuroprosthetics, and cognitive assistance devices, providing real-time, low-power processing for advanced
medical applications.
Advances in Autonomous Systems
As Neuromorphic Computing, drones, and robotics constantly expand, neuromorphic chips can deliver efficient real-time decision-making and sensor data processing, opening up opportunities for better autonomy and less energy consumption in these systems.
Trends
Integration with AI and Machine Learning
Neuromorphic computing is being combined largely with AI and machine learning models to improve real-time data processing, providing more efficient solutions for tasks like pattern recognition, anomaly detection, and decision-making in autonomous systems.
Miniaturization for Edge Devices
There is a major trend toward developing smaller, energy-efficient neuromorphic chips customized for edge computing devices, allowing real-time processing directly on IoT devices, wearables, and smart sensors without depending on cloud infrastructure.
Research Scope and Analysis
By Offering
The hardware segment is expected to dominate the neuromorphic computing market in 2024, holding a major revenue share, which is largely driven by the growth in the usage of neuromorphic hardware to accelerate computation in embedded devices. In addition, the integration of machine learning algorithms into these devices has contributed to the rising demand for hardware solutions. Government initiatives and higher investments are also driving the development of neuromorphic hardware, further expanding its role in the market.
Further, the software segment is expected to experience the highest growth rate in the coming years, which is due to the growing need for specialized software across numerous industries, like IT, aerospace, defense, telecom, and healthcare. Neuromorphic computing software applications, like data modeling, real-time data streaming, and forecasting, are gaining traction, helping boost the segment’s market share.
Moreover, several companies are offering complete neuromorphic solutions like hardware, software, and consulting services to help organizations and researchers explore and develop this technology for diverse applications. Well-known companies like IBM, Intel, and Qualcomm are leading in providing these solutions, assisting further advancements in neuromorphic computing technology.
By Deployment
The neuromorphic computing market based on deployment is divided into edge and cloud deployment, with the edge segment expected to see major growth in the coming years. Edge computing brings data processing closer to where the data is generated, minimizing the time it takes for information to travel to and from centralized servers, which is mainly important for applications that demand real-time responses, like Neuromorphic Computing &industrial automation.
By processing data at the source, edge computing also minimizes the demand to send sensitive information over networks, which assists in enhancing privacy and security, which minimizes the exposure to potential cyber threats, as the data remains closer to where it is collected. Overall, edge deployment offers faster, more secure processing for a variety of critical applications.
By Application
The neuromorphic computing market is categorized into several key applications: signal processing, image processing, data processing, object detection, and more. Among these, image processing is predicted to lead the market in 2024, as they are mainly effective at handling image data due to their inherent parallel processing capabilities, which align well with the demands of image recognition tasks, allowing for efficient and accurate processing of visual information.
Further, object detection is emerging as the most rapidly growing segment in the market. The strength of neuromorphic computing depends in its ability to learn and adapt, which improves its performance in identifying complex objects within images, which makes it highly valuable in areas like Neuromorphic Computing and security systems, where precise object detection is critical. As the technology constantly advances, its ability to efficiently handle complex visual tasks is driving the better adoption and development in these fields, positioning object detection as a major area of growth within neuromorphic computing.
By End Users
The neuromorphic computing market is analyzed across various industries, like automotive, healthcare, consumer electronics, manufacturing, aerospace & defense, and others. Among these, the consumer electronics segment is expected to have the largest market share, driven by the large usage of laptops, PCs, smartphones, tablets, and wearable smart devices.
As per Cisco, the number of connected wearable devices worldwide reached 1.1 billion in 2022, and the demand for smaller integrated circuits in these gadgets continues to grow. Neuromorphic chips, which provide efficient and compact solutions, are being largely adopted in smartphones and other sensory devices, driving the growth in the consumer electronics sector. Further, the automotive industry is projected to have the highest growth rate over the forecast period.
Neuromorphic technology majorly boosts the capabilities of
artificial intelligence (AI) and
machine learning (ML) systems in this industry, allowing advanced applications like autonomous driving,
natural language processing, and image recognition. These technologies are important for the future of smart and self-driving vehicles, and the adoption of neuromorphic computing is anticipated to drive significant innovation and growth in the automotive sector.
The Neuromorphic Computing Market Report is segmented on the basis of the following
By Offering
- Hardware
- Software
- Services
By Deployment
- Edge Computing
- Cloud Computing
By Application
- Signal Processing
- Image Processing
- Data Processing
- Object Detection
- Others
By End User
- Consumer Electronics
- Automotive
- Military & Defense
- Healthcare
- Others
Regional Analysis
North America is set to lead the neuromorphic computing market,
holding around 38.5% of the revenue share in 2024. The US and Canada are early adopters of this technology and are in the lead of applying neuromorphic computing systems.
A major trend driving growth in the region is the integration of AI-based voice and speech recognition technologies, which has allowed companies to fine-tune their speech recognition systems, providing a more refined and accurate voice experience for users.
As a result, the need for neuromorphic computing in North America continues to grow, with its applications becoming increasingly widespread. Further, Europe is also expected to see major growth in the neuromorphic computing market over the coming years.
Numerous initiatives and organizations across Europe are highly working on advancing the development and adoption of this technology. The region’s increase in the use of biometric technologies is contributing to new opportunities for neuromorphic computing, mainly in image processing applications.
With an aim on expanding the use of neuromorphic systems in areas like biometry, Europe is becoming a major player in the global neuromorphic computing landscape. The ongoing research and development in this field present exciting opportunities for organizations and researchers to contribute to this rapidly evolving technology.
Europe
- Germany
- The U.K.
- France
- Italy
- Russia
- Spain
- Benelux
- Nordic
- Rest of Europe
Asia-Pacific
- China
- Japan
- South Korea
- India
- ANZ
- ASEAN
- Rest of Asia-Pacific
Latin America
- Brazil
- Mexico
- Argentina
- Colombia
- Rest of Latin America
Middle East & Africa
- Saudi Arabia
- UAE
- South Africa
- Israel
- Egypt
- Rest of MEA
Competitive Landscape
Neuromorphic computing is a growing field where many research labs and tech companies are innovating brain-inspired hardware to enhance artificial intelligence. The aim is to create chips that can process data efficiently, using less power than traditional processors.
Competition is strong as organizations race to bring this technology to applications like robotics, smart sensors, and autonomous systems. While progress is steady, broad adoption is still in its early stages, with companies experimenting to find practical uses that outperform conventional AI methods.
Some of the prominent players in the Global Neuromorphic Computing are
- IBM Corp
- Intel Corp
- Qualcomm Technologies
- General Vision Inc
- Brain Corporation
- Samsung Electronics
- Known Inc
- Hewlett Packard Company
- Vicarious
- Applied Brain Research
- Other Key Players
Recent Developments
- In September 2024, Researchers at the Indian Institute of Science (IISc) announced that they developed a brain-inspired analog computing platform capable of storing & processing data in a large 16,500 conductance states within a molecular film, which also shows a stride forward over traditional digital computers in which data storage and processing are limited to just two states.
- In July 2024, The European Union and the Republic of Korea reported assisting four jointly funded projects in semiconductors as a deliverable of the EU-Republic of Korea Digital Partnership, which will develop heterogeneous integration technologies, i.e., technologies integrating multiple components onto one chip along with neuromorphic computing technologies and technologies imitating the functioning of the human brain.
- In May 2024, SpiNNcloud Systems GmbH unveiled the first commercially available neuromorphic supercomputer by launching its SpiNNaker2 platform, a supercomputer-level hybrid artificial intelligence high-performance computer system based on principles of the human brain, by using a large number of low-power processors to compute AI and other workloads efficiently.
- In April 2024, Intel unveiled that it built the world's largest neuromorphic system. Code-named Hala Point, deployed at Sandia National Laboratories, using Intel’s Loihi 2 processor, focused on assisting research for future brain-inspired artificial intelligence (AI), and resolving challenges related to the efficiency and sustainability of current AI.
- In April 2024, KAIST researchers announced the creation of a low-power, cost-efficient phase change memory device that can be utilized to substitute existing memory or used in implementing neuromorphic computing for next-generation AI hardware for its low processing costs & ultra-low power consumption.
Report Details
| Report Characteristics |
| Market Size (2024) |
USD 6.7 Bn |
| Forecast Value (2033) |
USD 55.6 Bn |
| CAGR (2024-2033) |
26.4% |
| Historical Data |
2018 – 2023 |
| The US Market Size (2024) |
USD 2.3 Bn |
| Forecast Data |
2025 – 2033 |
| Base Year |
2023 |
| Estimate Year |
2024 |
| Report Coverage |
Market Revenue Estimation, Market Dynamics, Competitive Landscape, Growth Factors and etc. |
| Segments Covered |
By Offering (Hardware, Software, and Services), By Deployment (Edge Computing and Cloud Computing), By Application (Signal Processing, Image Processing, Data Processing, Object Detection, and Others), By End User (Consumer Electronics, Automotive, Military & Defense, Healthcare, and Others) |
| Regional Coverage |
North America – The US and Canada; Europe – Germany, The UK, France, Russia, Spain, Italy, Benelux, Nordic, & Rest of Europe; Asia- Pacific– China, Japan, South Korea, India, ANZ, ASEAN, Rest of APAC; Latin America – Brazil, Mexico, Argentina, Colombia, Rest of Latin America; Middle East & Africa – Saudi Arabia, UAE, South Africa, Turkey, Egypt, Israel, & Rest of MEA
|
| Prominent Players |
IBM Corp, Intel Corp, Qualcomm Technologies, General Vision Inc, Brain Corporation, Samsung Electronics, Known Inc, Hewlett Packard Company, Vicarious, Applied Brain Research, and Other Key Players |
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