뉴로모픽 컴퓨팅 시장 : 오퍼링, 컴퓨팅 모델, 애플리케이션, 배포, 최종사용자별 - 세계 예측(2025-2030년)

The Neuromorphic Computing Market was valued at USD 1.91 billion in 2023 and is projected to grow to USD 2.33 billion in 2024, with a CAGR of 24.92%, reaching USD 9.09 billion by 2030.

Neuromorphic computing represents a groundbreaking evolution in the design and function of modern computing systems. By mimicking the neural architecture of the human brain, this technology is paving the way for systems that are not only faster but also capable of learning and adapting in real time. The paradigm shifts away from traditional von Neumann architectures towards bio-inspired models have opened up new possibilities in processing efficiency and energy consumption. Recent developments in both hardware and software have accelerated the adoption of neuromorphic principles, enabling more intuitive data processing across various applications. Researchers and industry experts commend the technology for its ability to process complex tasks using simplified computational models, which in turn fosters innovation across domains from artificial intelligence to robotics. As the market evolves, stakeholders from multiple industries are keenly observing these advances, recognizing that the integration of neuromorphic computing could redefine competitive landscapes and drive a new era of digital transformation. With its potential to revolutionize everything from sensor designs to computing cores, neuromorphic computing stands as a beacon of promise in next-generation technology solutions.

As the science matures, further integration of neural dynamics and adaptive processing is expected to underpin future smart systems, setting the stage for transformative changes in both commercial and industrial segments.

Transformative Shifts in the Neuromorphic Landscape

Over the past few years, the neuromorphic computing landscape has witnessed transformative shifts that challenge conventional computing paradigms. Leveraging bio-inspired architectures, the market is rapidly evolving with developments in both specialized hardware and advanced software frameworks. Innovations in circuit design and materials science have given rise to adaptable processors that mimic the synaptic functionality of biological brains. These breakthroughs are not isolated; they represent a synergistic reimagining of computing where efficiency, adaptability, and speed are paramount. Traditional data processing methods have been replaced by systems that incorporate dynamic learning capabilities and intuitional decision-making processes. Manufacturers are increasingly focused on integrating sensors and processors that work in tandem, creating systems that can make real-time decisions in critical environments such as autonomous vehicles or medical diagnostics.

Furthermore, the demand for solutions that can operate in both centralized and distributed environments is increasing. The flexibility to deploy these technologies in cloud and edge settings is rapidly becoming a key competitive differentiator. The ongoing convergence of interdisciplinary technologies, ranging from electronics to cognitive sciences, is fueling a paradigm shift that positions neuromorphic computing as a key driver of future innovation and disruption.

Key Segmentation Insights in Neuromorphic Computing

A comprehensive analysis of the neuromorphic computing market reveals several critical segmentation perspectives that shape its dynamic landscape. The market is first segmented based on offering, with investigations focusing on neuromorphic hardware and software. Within the hardware domain, special attention is given to neuromorphic processors and sensors, both of which underpin system efficiency and accuracy. Moving beyond physical components, segmentation based on computing models uncovers a rich array of simulation frameworks. From dynamic synapse models to implementations of the FitzHugh-Nagumo, Hodgkin-Huxley, Izhikevich, and Leaky Integrate-and-Fire models, as well as spiking neural networks, these approaches provide valuable insights into the emulation of neural behaviors.

Furthermore, segmentation by application has illuminated key use cases such as data processing, image processing, object detection, and signal processing, each illustrating the versatility and robustness of neuromorphic designs. The deployment segmentation highlights how solutions are adapted for both cloud-based and edge environments, ensuring scalability and versatility. Lastly, the segmentation by end-users spans a diverse array of industries including aerospace and defense, automotive and transportation, BFSI, consumer electronics, energy, healthcare and medical devices, IT and telecommunications, and manufacturing. Together, these segmentation insights not only clarify market trends but also offer a nuanced understanding of evolving consumer demands and technological capabilities in neuromorphic computing.

Based on Offering, market is studied across Neuromorphic Hardware and Software. The Neuromorphic Hardware is further studied across Neuromorphic Processor and Sensor.

Based on Computing Models, market is studied across Dynamic Synapse Models, FitzHugh-Nagumo Model, Hodgkin-Huxley Model, Izhikevich Model, Leaky Integrate-and-Fire Model, and Spiking Neural Networks.

Based on Application, market is studied across Data Processing, Image Processing, Object Detection, and Signal Processing.

Based on Deployment, market is studied across Cloud and Edge.

Based on End-Users, market is studied across Aerospace & Defense, Automotive & Transportation, BFSI, Consumer Electronics, Energy, Healthcare & Medical Devices, IT & Telecommunications, and Manufacturing.

Key Regional Insights into Global Developments

The international neuromorphic computing market is characterized by diverse regional trends that reflect varying adoption rates and technological capabilities. In the Americas, robust investments in research and development coupled with strong industrial collaborations are propelling forward-thinking initiatives that are quickly translating into commercial applications. Meanwhile, the Europe, Middle East & Africa region is marked by a strategic focus on next-generation technologies, with public-private partnerships driving innovation across multiple sectors and a commitment to regulatory-friendly ecosystems. The Asia-Pacific region stands out due to rapid economic growth and a surge in technological experimentation, where leading nations are investing heavily in both academic and industrial research. This region is rapidly becoming a global hub for pioneering work in neuromorphic hardware and software integrations.

These regional insights underscore the fact that, regardless of geography, stakeholders must navigate distinct market dynamics, regulatory environments, and consumer expectations. The interplay between local strengths and global trends is catalyzing cross-border collaborations and accelerating the diffusion of neuromorphic technologies worldwide, ultimately paving the way for a more interconnected and efficient future in computing.

Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

Key Companies Pioneering Neuromorphic Innovations

The competitive landscape within neuromorphic computing is both diverse and dynamic with a range of established names and emerging startups pushing the boundaries of technology. Leading companies such as aiMotive by Stellantis N.V. and Applied Brain Research are at the forefront with innovative research that bridges the gap between theoretical potential and practical application. Firms like Aspinity, Inc. and BrainChip, Inc. have been instrumental in the commercialization of advanced neural architectures, while Chengdu SynSense Technology Co., Ltd and DEEPX contribute significantly to the hardware advancements that power real-time data processing.

Notable players including General Vision Inc. and Hailo Technologies Ltd. complement these efforts by merging deep learning algorithms with next-generation sensor technologies. Industry giants such as Hewlett Packard Enterprise Company, Imec International, and Intel Corporation continue to invest in neuromorphic platforms that enhance system integration and scalability, in tandem with International Business Machines Corporation and Kneron, Inc. who are vocal proponents of sustainable and adaptive computing. MediaTek Inc. and Mythic, Inc. are known for integrating robust software solutions into neuromorphic processors, while Numenta, Inc. and Prophesee S.A. offer deep insights into algorithmic efficiencies that mirror biological processing. Furthermore, established technology leaders like Qualcomm Technologies, Inc., Robert Bosch GmbH, and Samsung Electronics Co., Ltd. are strategically positioning themselves within this domain.

Additional key players such as SK Hynix Inc., SpiNNcloud Systems GmbH, Syntiant Corp., and Toshiba Corporation further bolster market competitiveness by continuously driving innovation and expanding the boundary of what is technologically possible. Their collective contributions offer a well-rounded view of market trends, ensuring that the ecosystem remains vibrant and forward-thinking.

The report delves into recent significant developments in the Neuromorphic Computing Market, highlighting leading vendors and their innovative profiles. These include aiMotive by Stellantis N.V., Applied Brain Research, Aspinity, Inc., BrainChip, Inc., Chengdu SynSense Technology Co., Ltd, DEEPX, General Vision Inc., Hailo Technologies Ltd., Hewlett Packard Enterprise Company, Imec International, iniLabs Ltd., Innatera Nanosystems BV, Intel Corporation, International Business Machines Corporation, Kneron, Inc., MediaTek Inc., Mythic, Inc., Numenta, Inc., Prophesee S.A., Qualcomm Technologies, Inc., Robert Bosch GmbH, Samsung Electronics Co., Ltd., SK Hynix Inc., SpiNNcloud Systems GmbH, Syntiant Corp., and Toshiba Corporation. Actionable Recommendations for Industry Leaders

For decision-makers and industry pioneers, the current landscape of neuromorphic computing presents both opportunities and challenges that require strategic foresight. It is imperative to prioritize continued investment in research and development to stay ahead of rapid technological advancements. Leaders should consider forming strategic partnerships with research institutions to jointly explore emerging neuromorphic models and their potential real-world applications. Beyond technology development, embracing a customer-centric approach that focuses on measurable improvements in efficiency, power consumption, and system adaptability can differentiate one's offerings in a competitive market.

Furthermore, companies should explore diverse deployment strategies, by leveraging both cloud and edge solutions to optimize performance across different use cases. A holistic integration of advanced computing models-from dynamic synapse configurations to spiking neural networks-will enable organizations to harness the full capabilities of bio-inspired systems. Engaging early with regulatory bodies and establishing frameworks for compliance can also mitigate potential risks associated with technological innovations. Overall, a balanced approach that drives both technology enhancement and market penetration will position industry leaders for sustained success in the evolving landscape of neuromorphic computing.

Conclusion and Future Outlook

The executive summary of neuromorphic computing underscores an industry at the cusp of a major technological renaissance. The converging trends in hardware and software innovations, coupled with deep integration of bio-inspired models, present a transformative opportunity for industries worldwide. This surge in innovation is not only redefining the way computing systems are conceived but also setting a new benchmark for efficiency and adaptive learning. As the market segments diversify and regional initiatives strengthen, the promise of neuromorphic computing is becoming increasingly clear and compelling.

Looking forward, stakeholders must remain agile and forward-thinking, investing in novel architectures and aligning product strategies with emerging market demands. The capacity to seamlessly integrate advanced computing models into practical applications will be a key determinant of success. Ultimately, the journey toward fully realizing the potential of neuromorphic computing is driven by relentless innovation, collaborative spirit, and an unwavering commitment to excellence in technology and customer service.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Growing advancements in artificial intelligence and machine learning
    • 5.1.1.2. Increasing integration of smart sensors and IoT devices is enhancing the utility and adoption of neuromorphic systems
  • 5.1.2. Restraints
    • 5.1.2.1. Data security and privacy concerns along with software compatibility issues with existing digital architectures in neuromorphic devices
  • 5.1.3. Opportunities
    • 5.1.3.1. Government and private funding in neuromorphic research
    • 5.1.3.2. Collaborations and partnerships between tech giants and research organizations are propelling innovations in neuromorphic computing
    • 5.1.3.3. Emergence of neuromorphic architectures in gaming for lifelike virtual experiences
  • 5.1.4. Challenges
    • 5.1.4.1. Lack of standardized benchmarks for neuromorphic computing performance evaluation with confined accessibility
• 5.2. Market Segmentation Analysis
  • 5.2.1. Computing Models: Growing adoption of neuromorphic computing's Hodgkin-Huxley model in the biomedical sector
  • 5.2.2. Offering: Burgeoning usage of the software enhances the process by configuring adaptable neural networks and optimizing algorithms for specific applications
  • 5.2.3. Application: Expanding application of neuromorphic computing in data processing
  • 5.2.4. Deployment: Significant edge-based deployment of neuromorphic computing
  • 5.2.5. End-Users: Rising usage of the neuromorphic-computing in consumer electronics
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Neuromorphic Computing Market, by Offering

• 6.1. Introduction
• 6.2. Neuromorphic Hardware
  • 6.2.1. Neuromorphic Processor
  • 6.2.2. Sensor
• 6.3. Software

7. Neuromorphic Computing Market, by Computing Models

• 7.1. Introduction
• 7.2. Dynamic Synapse Models
• 7.3. FitzHugh-Nagumo Model
• 7.4. Hodgkin-Huxley Model
• 7.5. Izhikevich Model
• 7.6. Leaky Integrate-and-Fire Model
• 7.7. Spiking Neural Networks

8. Neuromorphic Computing Market, by Application

• 8.1. Introduction
• 8.2. Data Processing
• 8.3. Image Processing
• 8.4. Object Detection
• 8.5. Signal Processing

9. Neuromorphic Computing Market, by Deployment

• 9.1. Introduction
• 9.2. Cloud
• 9.3. Edge

10. Neuromorphic Computing Market, by End-Users

• 10.1. Introduction
• 10.2. Aerospace & Defense
• 10.3. Automotive & Transportation
• 10.4. BFSI
• 10.5. Consumer Electronics
• 10.6. Energy
• 10.7. Healthcare & Medical Devices
• 10.8. IT & Telecommunications
• 10.9. Manufacturing

11. Americas Neuromorphic Computing Market

• 11.1. Introduction
• 11.2. Argentina
• 11.3. Brazil
• 11.4. Canada
• 11.5. Mexico
• 11.6. United States

12. Asia-Pacific Neuromorphic Computing Market

• 12.1. Introduction
• 12.2. Australia
• 12.3. China
• 12.4. India
• 12.5. Indonesia
• 12.6. Japan
• 12.7. Malaysia
• 12.8. Philippines
• 12.9. Singapore
• 12.10. South Korea
• 12.11. Taiwan
• 12.12. Thailand
• 12.13. Vietnam

13. Europe, Middle East & Africa Neuromorphic Computing Market

• 13.1. Introduction
• 13.2. Denmark
• 13.3. Egypt
• 13.4. Finland
• 13.5. France
• 13.6. Germany
• 13.7. Israel
• 13.8. Italy
• 13.9. Netherlands
• 13.10. Nigeria
• 13.11. Norway
• 13.12. Poland
• 13.13. Qatar
• 13.14. Russia
• 13.15. Saudi Arabia
• 13.16. South Africa
• 13.17. Spain
• 13.18. Sweden
• 13.19. Switzerland
• 13.20. Turkey
• 13.21. United Arab Emirates
• 13.22. United Kingdom

14. Competitive Landscape

• 14.1. Market Share Analysis, 2023
• 14.2. FPNV Positioning Matrix, 2023
• 14.3. Competitive Scenario Analysis
  • 14.3.1. Seoul National University unveils ultra-low power neuromorphic hardware advancing AI Computation
  • 14.3.2. MediaTek launches Dimensity 9400 chip enhancing AI, gaming, and efficiency to rival Qualcomm's leadership
  • 14.3.3. IISc scientists are transforning data processing with neuromorphic computing technology
  • 14.3.4. Innatera's USD 21 million Series enehnces edge AI with Spiking Neural Processor T1
  • 14.3.5. Ultraleap, Prophesee, and TCL RayNeo redefine usability with low-power neuromorphic systems
  • 14.3.6. Intel's advancements at Computex 2024 redefine AI integration and power efficiency with new processor releases
  • 14.3.7. Launch of SpiNNaker2 marks a new era in neuromorphic computing for scalable and energy-efficient AI solutions
  • 14.3.8. Intel unveils Hala Point to reshape AI with unprecedented neuromorphic capabilities for efficiency
  • 14.3.9. SynSense and iniVation merger creates powerful neuromorphic leader driving innovation in intelligent vision technologies across diverse markets
  • 14.3.10. Innatera's Spiking Neural Processor T1 revolutionizes ultra-low power AI for sensor-edge applications at CES 2024
• 14.4. Strategy Analysis & Recommendation
  • 14.4.1. MediaTek Inc.
  • 14.4.2. PROPHESEE S.A.
  • 14.4.3. Intel Corporation
  • 14.4.4. Innatera Nanosystems

Companies Mentioned

• 1. aiMotive by Stellantis N.V.
• 2. Applied Brain Research
• 3. Aspinity, Inc.
• 4. BrainChip, Inc.
• 5. Chengdu SynSense Technology Co., Ltd
• 6. DEEPX
• 7. General Vision Inc.
• 8. Hailo Technologies Ltd.
• 9. Hewlett Packard Enterprise Company
• 10. Imec International
• 11. iniLabs Ltd.
• 12. Innatera Nanosystems BV
• 13. Intel Corporation
• 14. International Business Machines Corporation
• 15. Kneron, Inc.
• 16. MediaTek Inc.
• 17. Mythic, Inc.
• 18. Numenta, Inc.
• 19. Prophesee S.A.
• 20. Qualcomm Technologies, Inc.
• 21. Robert Bosch GmbH
• 22. Samsung Electronics Co., Ltd.
• 23. SK Hynix Inc.
• 24. SpiNNcloud Systems GmbH
• 25. Syntiant Corp.
• 26. Toshiba Corporation