The Global Market for Neuromorphic Computing and Sensing 2025-2035

1.1        Overview of the neuromorphic computing and sensing market
1.1.1     Global Market Revenues 2024-2035
1.1.2     Market segmentation
1.2        Ending of Moore’s Law
1.3        Historical market
1.4        The market in 2024
1.5        Key market trends and growth drivers
1.6        Market challenges and limitations
1.7        Future outlook and opportunities
1.7.1     Emerging trends
1.7.1.1  Hybrid Neuromorphic-Conventional Computing and Sensing Systems
1.7.1.2  Edge AI and IoT
1.7.1.3  Quantum Computing
1.7.1.4  Explainable AI
1.7.1.5  Brain-Computer Interfaces
1.7.1.6  Energy-efficient AI at scale
1.7.1.7  Real-time learning and adaptation
1.7.1.8  Enhanced Perception Systems
1.7.1.9  Large-scale Neuroscience Simulations
1.7.1.10  Secure, Decentralized AI
1.7.1.11  Robotics that mimic humans
1.7.1.12  Neural implants for healthcare
1.7.1.13  New Application Areas and Use Cases
1.7.1.14  Disruptive Business Models and Services
1.7.1.15  Collaborative Ecosystem Development
1.7.1.16  Skill Development and Workforce Training
1.7.2     Technology roadmap
1.8        Neuromorphic computing and generative AI
1.9        Market value chain
1.10      Market map
1.11      Funding and investments
1.12      Strategic Partnerships and Collaborations
1.13      Regulatory and Ethical Considerations
1.13.1   Data Privacy and Security
1.13.2   Bias and Fairness in Neuromorphic Systems
1.13.3   Intellectual Property and Patent Landscape
1.14      Sustainability and Environmental Impact
1.14.1   Carbon Footprint Analysis of Neuromorphic Systems
1.14.2   Energy Efficiency Metrics and Benchmarking
1.14.3   Green Manufacturing Practices
1.14.4   End-of-life and Recycling Considerations
1.14.5   Environmental Regulations Compliance

2.1        Definition and concept of neuromorphic computing and sensing
2.2        Main neuromorphic approaches
2.2.1     Large-scale hardware neuromorphic computing systems
2.2.2     Non-volatile memory technologies
2.2.3     Advanced memristive materials and devices
2.3        Fabrication Processes for Neuromorphic Systems
2.4        Key Material Suppliers
2.5        Supply Chain Vulnerabilities and Mitigation
2.6        Manufacturing Capacity Analysis
2.7        Quality Control and Testing Procedures
2.8        Comparison with traditional computing and sensing approaches
2.9        Neuromorphic computing vs. quantum computing
2.10      Key features and advantages
2.10.1   Low latency and real-time processing
2.10.2   Power efficiency and energy savings
2.10.3   Scalability and adaptability
2.10.4   Online learning and autonomous decision-making
2.11      Markets and Applications
2.11.1   Edge AI and IoT
2.11.2   Autonomous Vehicles and Robotics
2.11.3   Cybersecurity and Anomaly Detection
2.11.4   Smart Sensors and Monitoring Systems
2.11.5   Datacenter and High-Performance Computing

3.1        Spiking Neural Networks (SNNs)
3.1.1     Biological inspiration and principles
3.1.2     Types of SNNs and their characteristics
3.1.3     Advantages and limitations of SNNs
3.2        Memory Architectures for Neuromorphic Computing
3.2.1     Conventional memory approaches (SRAM, DRAM)
3.2.2     Emerging non-volatile memory (eNVM) technologies
3.2.2.1  Phase-Change Memory (PCM)
3.2.2.2  Resistive RAM (RRAM)
3.2.2.3  Magnetoresistive RAM (MRAM)
3.2.2.4  Ferroelectric RAM (FeRAM)
3.2.3     In-memory computing and near-memory computing
3.2.4     Hybrid memory architectures
3.3        Neuromorphic Hardware and Processors
3.3.1     Digital neuromorphic processors
3.3.2     Analog neuromorphic processors
3.3.3     Mixed-signal neuromorphic processors
3.3.4     FPGA-based neuromorphic systems
3.3.5     Neuromorphic accelerators and co-processors
3.4        Software and Frameworks for Neuromorphic Computing
3.4.1     Neuromorphic programming languages and tools
3.4.2     Neuromorphic simulation platforms and frameworks
3.4.3     Neuromorphic algorithm libraries and repositories
3.4.4     Neuromorphic software development kits (SDKs)

4.1        Event-Based Sensors and Processing
4.1.1     Neuromorphic vision sensors
4.1.2     Neuromorphic auditory sensors
4.1.3     Neuromorphic olfactory sensors
4.1.4     Event-driven processing and algorithms
4.2        Hybrid Sensing Approaches
4.2.1     Combination of conventional and event-based sensors
4.2.2     Fusion of multiple sensing modalities
4.2.3     Advantages and challenges of hybrid sensing
4.3        Neuromorphic Sensor Architectures and Designs
4.3.1     Pixel-level processing and computation
4.3.2     Sensor-processor co-design and integration
4.3.3     Bio-inspired sensor designs and materials
4.4        Signal Processing and Feature Extraction Techniques
4.4.1     Spike-based Encoding and Decoding
4.4.2     Temporal and Spatiotemporal Feature Extraction
4.4.3     Neuromorphic Filtering and Denoising
4.4.4     Adaptive and Learning-Based Processing

5.1        Mobile and Consumer Applications
5.1.1     Smartphones and wearables
5.1.2     Smart home and IoT devices
5.1.3     Consumer health and wellness
5.1.4     Entertainment and gaming
5.2        Automotive and Transportation
5.2.1     Advanced Driver Assistance Systems (ADAS)
5.2.2     Autonomous vehicles and robotaxis
5.2.3     Vehicle infotainment and user experience
5.2.4     Smart traffic management and infrastructure
5.3        Industrial and Manufacturing
5.3.1     Industrial IoT and smart factories
5.3.2     Predictive maintenance and anomaly detection
5.3.3     Quality control and inspection
5.3.4     Logistics and supply chain optimization
5.4        Healthcare and Medical Devices
5.4.1     Medical imaging and diagnostics
5.4.2     Wearable health monitoring devices
5.4.3     Personalized medicine and drug discovery
5.4.4     Assistive technologies and prosthetics
5.5        Aerospace and Defense
5.5.1     Unmanned Aerial Vehicles (UAVs) and drones
5.5.2     Satellite imaging and remote sensing
5.5.3     Missile guidance and target recognition
5.5.4     Cybersecurity and threat detection:
5.6        Datacenters and Cloud Services
5.6.1     High-performance computing and scientific simulations
5.6.2     Big data analytics and machine learning
5.6.3     Cloud-based AI services and platforms
5.6.4     Energy-efficient datacenter infrastructure
5.7        Regional Market Analysis and Forecasts
5.7.1     North America
5.7.2     Europe
5.7.3     Asia-Pacific
5.7.4     Rest of the World
5.8        Competitive Landscape and Key Players
5.8.1     Overview of the Neuromorphic Computing and Sensing Ecosystem
5.8.2     Neuromorphic Chip Manufacturers and Processors
5.8.3     Neuromorphic Sensor Manufacturers
5.8.4     Emerging Non-Volatile Memory (eNVM) Manufacturers
5.8.5     Neuromorphic Software and Framework Providers
5.8.6     Research Institutions and Academia
5.9        Competing Emerging Technologies
5.9.1     Quantum Computing
5.9.2     Photonic Computing
5.9.3     DNA Computing
5.9.4     Spintronic Computing
5.9.5     Chemical Computing
5.9.6     Superconducting Computing
5.9.7     Analog AI Chips
5.9.8     In-Memory Computing
5.9.9     Reversible Computing
5.9.10   Quantum Dot Computing
5.9.11   Technology Substitution Analysis
5.9.12   Migration Pathways
5.9.13   Comparative Advantages/Disadvantages

Table 1. Overview of the neuromorphic computing and sensing market.
Table 2. Global market for neuromorphic computing and sensors, 2024-2035 (Millions USD).
Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2035.
Table 4. Key market trends and growth drivers.
Table 5. Market challenges and limitations.
Table 6. Neuromorphic computing and generative AI strategies.
Table 7. Funding and investments in neuromorphic computing and sensing.
Table 8. Strategic Partnerships and Collaborations in the Neuromorphic Industry.
Table 9. Regulatory and Ethical Considerations of neuromorphic computing & sensing.
Table 10. Main neuromorphic sensing approaches.
Table 11. Main Neuromorphic Computing Approaches.
Table 12. Resistive Non-Volatile Memory (NVM) Technologies.
Table 13. Advanced Memristive Materials, Devices, and Novel Computation Concepts.
Table 14. Fabrication Processes for Neuromorphic Systems.
Table 15. Key Material Suppliers and Dependencies.
Table 16. Comparison with traditional computing and sensing approaches.
Table 17. Comparison between neuromorphic and quantum computing.
Table 18. Key features and advantages of neuromorphic computing and sensing.
Table 19. Markets and Applications of neuromorphic computing and sensing.
Table 20. Von neumann architecture versus neuromorphic architecture.
Table 21. Types of SNNs and their characteristics.
Table 22. Advantages and limitations of SNNs.
Table 23. Conventional memory approaches (SRAM, DRAM).
Table 24. Emerging non-volatile memory (eNVM) technologies.
Table 25. Hybrid memory architectures.
Table 26. Neuromorphic accelerators and co-processors.
Table 27. Neuromorphic programming languages and tools.
Table 28. Neuromorphic simulation platforms and frameworks.
Table 29. Neuromorphic algorithm libraries and repositories.
Table 30. Neuromorphic software development kits (SDKs).
Table 31. Hybrid sensing approaches.
Table 32. Advantages and challenges of hybrid sensing.
Table 33. Bio-inspired sensor designs and materials.
Table 34. Signal Processing and Feature Extraction Techniques.
Table 35. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application.
Table 36. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application.
Table 37. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application.
Table 38. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application.
Table 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD.
Table 40. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application.
Table 41. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application.
Table 42. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.
Table 43. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.
Table 44. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD.
Table 45. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.
Table 46. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.
Table 47. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application.
Table 48. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application.
Table 49. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD.
Table 50. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application.
Table 51. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application.
Table 52. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application.
Table 53. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application.
Table 54. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD.
Table 55. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application.
Table 56. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application.
Table 57. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application.
Table 58. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application.
Table 59. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD.
Table 60. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application.
Table 61. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application.
Table 62. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application.
Table 63. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application.
Table 64. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD.
Table 65. Market revenues for neuromorphic computing and sensing by region from 2024-2035 in millions USD.
Table 66. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD.
Table 67. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD.
Table 68. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2035), millions USD.
Table 69. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2035), millions USD.
Table 70. Neuromorphic Chip Manufacturers and Their Product Offerings.
Table 71. Neuromorphic Sensor Manufacturers and Their Product Offerings.
Table 72. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings.
Table 73. Neuromorphic Software and Framework Providers and Their Solutions.
Table 74. Key Research Institutions and Academia in Neuromorphic Computing and Sensing.
Table 75. Competing Emerging Technologies for Neuromorphic Computing and Sensing.
Table 76. Technology Substitution Analysis.
Table 77. Comparative Advantages/Disadvantages.
Table 78. Evolution of Apple Neural Engine.
Table 79. Dynex subscription plans.

Figure 1. Global market for neuromorphic computing and sensors, 2023-2035 (Millions USD).
Figure 2. Neuromorphic Computing and Sensing Market Segmentation 2020-2035.
Figure 3. Neuromorphic computing and sensing technology roadmap.
Figure 4. Market value chain for neuromorphic computing and sensing.
Figure 5. Neuromorphic computing and sensing market map.
Figure 6. Evolution of the main hardware technologies for neuromorphic computing.
Figure 7. Key materials in NVM technology for neuromorphic computing.
Figure 8. Advanced memristive materials for neuromorphic computing.
Figure 9. Neural networks in autonomous vehicles.
Figure 10.  Concept illustration of centralized and decentralized intelligence in robotics.
Figure 11.  Neuromorphic programmable robot with dynamic vision developed by SynSense.
Figure 12. Comparison of High-Level Conventional and Neuromorphic Memory Architectures.
Figure 13. Spiking Neural Network (SNN) Structure and Operation.
Figure 14. IBM TrueNorth Processor.
Figure 15. Event-Based Sensor Operation and Data Processing Flow.
Figure 16. Conventional sensor vs. Event-based sensor.
Figure 17. Operation of neuromorphic vision sensors.
Figure 18. Cyranose 320 Electronic Nose.
Figure 19. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information.
Figure 20. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications.
Figure 21. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD.
Figure 22. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation.
Figure 23. Sensors used by the ADAS (Advanced Driver-Assistance System).
Figure 24. Enabling technologies for autonomous vehicles.
Figure 25. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing.
Figure 26. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD.
Figure 27. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing.
Figure 28. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD.
Figure 29. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices.
Figure 30. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities.
Figure 31. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics.
Figure 32. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD.
Figure 33. Technology roadmap for neuromorphic computing and sensing in aerospace and defense.
Figure 34. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight.
Figure 35. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD.
Figure 36. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services.
Figure 37. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD.
Figure 38. Global Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD.
Figure 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD.
Figure 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, Asia-Pacific (2024-2035), millions USD.
Figure 41. Global Neuromorphic Computing and Sensing Market Size and Forecast, in in the Rest of the World (2024-2035), millions USD.
Figure 42. Neuromorphic Computing and Sensing Ecosystem Overview.
Figure 43. Cerebas WSE-2.
Figure 44. DeepX NPU DX-GEN1.
Figure 45. Google TPU.
Figure 46. GrAI VIP.
Figure 47. Groq Tensor Streaming Processor (TSP).
Figure 48. DVL-5000 neuromorphic laser profiler.
Figure 49. Spiking Neural Processor
Figure 50. TROOPER robot.
Figure 51. 11th Gen Intel® Core™ S-Series.
Figure 52.  Intel Loihi 2 chip.
Figure 53. Envise.
Figure 54. Pentonic 2000.
Figure 55. Azure Maia 100 and Cobalt 100 chips.
Figure 56. Mythic MP10304 Quad-AMP PCIe Card.
Figure 57. Nvidia H200 AI chip.
Figure 58. Grace Hopper Superchip.
Figure 59. Prophesee Metavision starter kit - AMD Kria KV260 and active marker LED board.
Figure 60. Cloud AI 100.
Figure 61. Overview of SpiNNaker2 architecture for the ”SpiNNcloud” cloud system and edge systems.
Figure 62. Untether AI chip.