The Global Neuromorphic Computing & Sensing Market 2025-2035 - Advanced and Emerging Technology Market Research

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Published: February 2025
Pages: 284
Tables: 79
Figures: 62

The market for neuromorphic computing and sensing is experiencing rapid growth driven by increasing demand for energy-efficient AI processing and real-time sensor applications. The market is currently segmented between neuromorphic computing hardware, which includes processors and memory systems, and neuromorphic sensing devices, particularly event-based vision sensors. Major semiconductor companies including Intel, IBM, and Samsung are commercializing neuromorphic processors, while specialized companies like Prophesee and iniVation lead in neuromorphic sensing technology. The ecosystem continues to expand with new entrants focusing on specific applications or technological approaches.

Key market drivers include the growing need for energy-efficient AI processing at the edge, increasing demands for real-time processing in autonomous systems, and the limitations of traditional computing architectures in meeting these requirements. The automotive sector represents a significant market opportunity, particularly for neuromorphic vision sensors in advanced driver assistance systems and autonomous vehicles. Industrial applications, including robotics and quality control, form another major market segment.

The technology's adoption is accelerating in several key verticals. Healthcare applications are emerging, particularly in medical imaging and neural interfaces. The aerospace and defense sector is investing in neuromorphic technology for autonomous systems and signal processing. Consumer electronics manufacturers are exploring neuromorphic solutions for next-generation mobile devices and IoT applications. Looking toward 2035, the market is expected to undergo significant transformation as the technology matures and new applications emerge. Technical advances in materials science and manufacturing processes are likely to reduce costs and improve performance, enabling broader adoption. The development of standardized development tools and software ecosystems will facilitate easier implementation and integration with existing systems.

Market growth faces several challenges, including the need for standardization, current high manufacturing costs, and competition from alternative technologies. However, the fundamental advantages of neuromorphic approaches in energy efficiency and real-time processing continue to drive investment and development. The emergence of hybrid systems combining neuromorphic elements with traditional computing architectures represents a significant trend that could accelerate market adoption.

Investment in the sector continues to grow, with both venture capital and corporate funding supporting technology development and commercialization. Strategic partnerships between technology providers, manufacturers, and end-users are becoming increasingly important for market development. The ecosystem is evolving to include specialized software providers, system integrators, and service companies supporting implementation and deployment. Future market growth will likely be influenced by advances in complementary technologies, including artificial intelligence, edge computing, and IoT. The integration of neuromorphic technology with these domains could create new application opportunities and market segments. Success in the market will require balancing technical innovation with practical implementation considerations and clear demonstration of value in specific applications.

The Global Market for Neuromorphic Computing and Sensing 2025-2035 is a comprehensive analysis of the rapidly evolving neuromorphic computing and sensing market, featuring detailed insights into market trends, technological developments, and growth opportunities through 2035. This extensive report examines the transformation of computing architectures from traditional von Neumann approaches to brain-inspired neuromorphic systems, providing crucial market forecasts and competitive intelligence. The report provides in-depth coverage of key technologies including spiking neural networks, event-based sensors, and advanced memory architectures. It analyzes major application areas such as autonomous vehicles, edge AI, healthcare, industrial automation, and data centers. The study includes detailed market forecasts from 2025-2035, segmented by technology, application, and geography.

Key features include:

Detailed market size and forecast data (2025-2035)
Analysis of 140+ companies shaping the industry. Companies profiled include BR, Advanced Micro Devices, AiM Future, AI Startek, AI Storm, AlpsenTek, Amazon Web Services, Ambarella, Apple, ARM, Axelera AI, Baidu, BrainChip, Cerebras Systems, Google, GrAI Matter Labs, Hailo, Huawei, IBM, Intel, Lightmatter, MediaTek, Meta, Microsoft, Mythic, Nvidia, NXP Semiconductors, Prophesee, Qualcomm, Rain AI, Samsung, SK Hynix, Sony, STMicroelectronics, Syntiant, TSMC, and many others actively developing neuromorphic technologies and solutions.
Comprehensive technology assessment and roadmaps
Deep dive into competing emerging technologies
Examination of manufacturing processes and supply chains
Environmental impact and sustainability analysis
Strategic insights into market opportunities and challenges
Regional market analysis covering North America, Europe, Asia-Pacific, and RoW

1 EXECUTIVE SUMMARY 17

1.1 Overview of the neuromorphic computing and sensing market 17
- 1.1.1 Global Market Revenues 2024-2035 18
- 1.1.2 Market segmentation 19
1.2 Ending of Moore’s Law 21
1.3 Historical market 22
1.4 The market in 2024 23
1.5 Key market trends and growth drivers 23
1.6 Market challenges and limitations 24
1.7 Future outlook and opportunities 26
- 1.7.1 Emerging trends 26
  - 1.7.1.1 Hybrid Neuromorphic-Conventional Computing and Sensing Systems 26
  - 1.7.1.2 Edge AI and IoT 26
  - 1.7.1.3 Quantum Computing 27
  - 1.7.1.4 Explainable AI 28
  - 1.7.1.5 Brain-Computer Interfaces 28
  - 1.7.1.6 Energy-efficient AI at scale 29
  - 1.7.1.7 Real-time learning and adaptation 29
  - 1.7.1.8 Enhanced Perception Systems 30
  - 1.7.1.9 Large-scale Neuroscience Simulations 30
  - 1.7.1.10 Secure, Decentralized AI 30
  - 1.7.1.11 Robotics that mimic humans 31
  - 1.7.1.12 Neural implants for healthcare 31
  - 1.7.1.13 New Application Areas and Use Cases 32
  - 1.7.1.14 Disruptive Business Models and Services 32
  - 1.7.1.15 Collaborative Ecosystem Development 32
  - 1.7.1.16 Skill Development and Workforce Training 33
- 1.7.2 Technology roadmap 34
1.8 Neuromorphic computing and generative AI 35
1.9 Market value chain 36
1.10 Market map 37
1.11 Funding and investments 38
1.12 Strategic Partnerships and Collaborations 39
1.13 Regulatory and Ethical Considerations 40
- 1.13.1 Data Privacy and Security 41
- 1.13.2 Bias and Fairness in Neuromorphic Systems 41
- 1.13.3 Intellectual Property and Patent Landscape 41
1.14 Sustainability and Environmental Impact 42
- 1.14.1 Carbon Footprint Analysis of Neuromorphic Systems 42
- 1.14.2 Energy Efficiency Metrics and Benchmarking 42
- 1.14.3 Green Manufacturing Practices 43
- 1.14.4 End-of-life and Recycling Considerations 43
- 1.14.5 Environmental Regulations Compliance 43

2 INTRODUCTION 44

2.1 Definition and concept of neuromorphic computing and sensing 44
2.2 Main neuromorphic approaches 45
- 2.2.1 Large-scale hardware neuromorphic computing systems 47
- 2.2.2 Non-volatile memory technologies 47
- 2.2.3 Advanced memristive materials and devices 48
2.3 Fabrication Processes for Neuromorphic Systems 49
2.4 Key Material Suppliers 49
2.5 Supply Chain Vulnerabilities and Mitigation 50
2.6 Manufacturing Capacity Analysis 50
2.7 Quality Control and Testing Procedures 50
2.8 Comparison with traditional computing and sensing approaches 51
2.9 Neuromorphic computing vs. quantum computing 51
2.10 Key features and advantages 53
- 2.10.1 Low latency and real-time processing 53
- 2.10.2 Power efficiency and energy savings 53
- 2.10.3 Scalability and adaptability 53
- 2.10.4 Online learning and autonomous decision-making 54
2.11 Markets and Applications 54
- 2.11.1 Edge AI and IoT 56
- 2.11.2 Autonomous Vehicles and Robotics 57
- 2.11.3 Cybersecurity and Anomaly Detection 59
- 2.11.4 Smart Sensors and Monitoring Systems 60
- 2.11.5 Datacenter and High-Performance Computing 61

3 NEUROMORPHIC COMPUTING TECHNOLOGIES AND ARCHITECTURE 63

3.1 Spiking Neural Networks (SNNs) 64
- 3.1.1 Biological inspiration and principles 65
- 3.1.2 Types of SNNs and their characteristics 65
- 3.1.3 Advantages and limitations of SNNs 65
3.2 Memory Architectures for Neuromorphic Computing 66
- 3.2.1 Conventional memory approaches (SRAM, DRAM) 66
- 3.2.2 Emerging non-volatile memory (eNVM) technologies 67
  - 3.2.2.1 Phase-Change Memory (PCM) 67
  - 3.2.2.2 Resistive RAM (RRAM) 67
  - 3.2.2.3 Magnetoresistive RAM (MRAM) 68
  - 3.2.2.4 Ferroelectric RAM (FeRAM) 68
- 3.2.3 In-memory computing and near-memory computing 69
- 3.2.4 Hybrid memory architectures 69
3.3 Neuromorphic Hardware and Processors 70
- 3.3.1 Digital neuromorphic processors 70
- 3.3.2 Analog neuromorphic processors 71
- 3.3.3 Mixed-signal neuromorphic processors 71
- 3.3.4 FPGA-based neuromorphic systems 72
- 3.3.5 Neuromorphic accelerators and co-processors 72
3.4 Software and Frameworks for Neuromorphic Computing 73
- 3.4.1 Neuromorphic programming languages and tools 73
- 3.4.2 Neuromorphic simulation platforms and frameworks 74
- 3.4.3 Neuromorphic algorithm libraries and repositories 75
- 3.4.4 Neuromorphic software development kits (SDKs) 76

4 NEUROMORPHIC SENSING TECHNOLOGIES AND ARCHITECTURES 77

4.1 Event-Based Sensors and Processing 77
- 4.1.1 Neuromorphic vision sensors 78
- 4.1.2 Neuromorphic auditory sensors 79
- 4.1.3 Neuromorphic olfactory sensors 80
- 4.1.4 Event-driven processing and algorithms 81
4.2 Hybrid Sensing Approaches 82
- 4.2.1 Combination of conventional and event-based sensors 83
- 4.2.2 Fusion of multiple sensing modalities 84
- 4.2.3 Advantages and challenges of hybrid sensing 84
4.3 Neuromorphic Sensor Architectures and Designs 85
- 4.3.1 Pixel-level processing and computation 85
- 4.3.2 Sensor-processor co-design and integration 85
- 4.3.3 Bio-inspired sensor designs and materials 86
4.4 Signal Processing and Feature Extraction Techniques 87
- 4.4.1 Spike-based Encoding and Decoding 88
- 4.4.2 Temporal and Spatiotemporal Feature Extraction 89
- 4.4.3 Neuromorphic Filtering and Denoising 90
- 4.4.4 Adaptive and Learning-Based Processing 90

5 MARKET ANALYSIS AND FORECASTS 92

5.1 Mobile and Consumer Applications 92
- 5.1.1 Smartphones and wearables 92
- 5.1.2 Smart home and IoT devices 93
- 5.1.3 Consumer health and wellness 94
- 5.1.4 Entertainment and gaming 95
5.2 Automotive and Transportation 97
- 5.2.1 Advanced Driver Assistance Systems (ADAS) 98
- 5.2.2 Autonomous vehicles and robotaxis 100
- 5.2.3 Vehicle infotainment and user experience 103
- 5.2.4 Smart traffic management and infrastructure 104
5.3 Industrial and Manufacturing 107
- 5.3.1 Industrial IoT and smart factories 108
- 5.3.2 Predictive maintenance and anomaly detection 109
- 5.3.3 Quality control and inspection 110
- 5.3.4 Logistics and supply chain optimization 111
5.4 Healthcare and Medical Devices 114
- 5.4.1 Medical imaging and diagnostics 115
- 5.4.2 Wearable health monitoring devices 116
- 5.4.3 Personalized medicine and drug discovery 117
- 5.4.4 Assistive technologies and prosthetics 119
5.5 Aerospace and Defense 121
- 5.5.1 Unmanned Aerial Vehicles (UAVs) and drones 122
- 5.5.2 Satellite imaging and remote sensing 124
- 5.5.3 Missile guidance and target recognition 125
- 5.5.4 Cybersecurity and threat detection: 126
5.6 Datacenters and Cloud Services 129
- 5.6.1 High-performance computing and scientific simulations: 130
- 5.6.2 Big data analytics and machine learning 131
- 5.6.3 Cloud-based AI services and platforms 133
- 5.6.4 Energy-efficient datacenter infrastructure 134
5.7 Regional Market Analysis and Forecasts 137
- 5.7.1 North America 137
- 5.7.2 Europe 138
- 5.7.3 Asia-Pacific 140
- 5.7.4 Rest of the World 141
5.8 Competitive Landscape and Key Players 143
- 5.8.1 Overview of the Neuromorphic Computing and Sensing Ecosystem 143
- 5.8.2 Neuromorphic Chip Manufacturers and Processors 143
- 5.8.3 Neuromorphic Sensor Manufacturers 144
- 5.8.4 Emerging Non-Volatile Memory (eNVM) Manufacturers 145
- 5.8.5 Neuromorphic Software and Framework Providers 145
- 5.8.6 Research Institutions and Academia 146
5.9 Competing Emerging Technologies 149
- 5.9.1 Quantum Computing 149
- 5.9.2 Photonic Computing 150
- 5.9.3 DNA Computing 150
- 5.9.4 Spintronic Computing 150
- 5.9.5 Chemical Computing 150
- 5.9.6 Superconducting Computing 151
- 5.9.7 Analog AI Chips 151
- 5.9.8 In-Memory Computing 151
- 5.9.9 Reversible Computing 151
- 5.9.10 Quantum Dot Computing 152
- 5.9.11 Technology Substitution Analysis 152
- 5.9.12 Migration Pathways 154
- 5.9.13 Comparative Advantages/Disadvantages 155

6 COMPANY PROFILES 156 (143 company profiles)

7 RESEARCH SCOPE & METHODOLOGY 278

8 REFERENCES 279

List of Tables

Table 1. Overview of the neuromorphic computing and sensing market. 17
Table 2. Global market for neuromorphic computing and sensors, 2024-2035 (Millions USD). 18
Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2035. 19
Table 4. Key market trends and growth drivers. 24
Table 5. Market challenges and limitations. 25
Table 6. Neuromorphic computing and generative AI strategies. 36
Table 7. Funding and investments in neuromorphic computing and sensing. 38
Table 8. Strategic Partnerships and Collaborations in the Neuromorphic Industry. 39
Table 9. Regulatory and Ethical Considerations of neuromorphic computing & sensing. 41
Table 10. Main neuromorphic sensing approaches. 44
Table 11. Main Neuromorphic Computing Approaches. 45
Table 12. Resistive Non-Volatile Memory (NVM) Technologies. 47
Table 13. Advanced Memristive Materials, Devices, and Novel Computation Concepts. 48
Table 14. Fabrication Processes for Neuromorphic Systems. 49
Table 15. Key Material Suppliers and Dependencies. 49
Table 16. Comparison with traditional computing and sensing approaches. 51
Table 17. Comparison between neuromorphic and quantum computing. 52
Table 18. Key features and advantages of neuromorphic computing and sensing. 53
Table 19. Markets and Applications of neuromorphic computing and sensing. 54
Table 20. Von neumann architecture versus neuromorphic architecture. 63
Table 21. Types of SNNs and their characteristics. 65
Table 22. Advantages and limitations of SNNs. 66
Table 23. Conventional memory approaches (SRAM, DRAM). 66
Table 24. Emerging non-volatile memory (eNVM) technologies. 67
Table 25. Hybrid memory architectures. 70
Table 26. Neuromorphic accelerators and co-processors. 73
Table 27. Neuromorphic programming languages and tools. 74
Table 28. Neuromorphic simulation platforms and frameworks. 74
Table 29. Neuromorphic algorithm libraries and repositories. 75
Table 30. Neuromorphic software development kits (SDKs). 76
Table 31. Hybrid sensing approaches. 83
Table 32. Advantages and challenges of hybrid sensing. 84
Table 33. Bio-inspired sensor designs and materials. 86
Table 34. Signal Processing and Feature Extraction Techniques. 87
Table 35. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application. 93
Table 36. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application. 93
Table 37. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application. 94
Table 38. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application. 96
Table 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD. 96
Table 40. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application. 99
Table 41. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application. 102
Table 42. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application. 104
Table 43. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application. 105
Table 44. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD. 105
Table 45. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application. 108
Table 46. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application. 110
Table 47. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application. 111
Table 48. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application. 112
Table 49. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD. 112
Table 50. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application. 115
Table 51. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application. 117
Table 52. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application. 118
Table 53. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application. 120
Table 54. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD. 120
Table 55. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application. 123
Table 56. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application. 125
Table 57. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application. 126
Table 58. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application. 127
Table 59. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD. 128
Table 60. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application. 130
Table 61. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application. 132
Table 62. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application. 133
Table 63. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application. 135
Table 64. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD. 135
Table 65. Market revenues for neuromorphic computing and sensing by region from 2024-2035 in millions USD. 137
Table 66. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD. 137
Table 67. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD. 138
Table 68. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2035), millions USD. 140
Table 69. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2035), millions USD. 141
Table 70. Neuromorphic Chip Manufacturers and Their Product Offerings. 144
Table 71. Neuromorphic Sensor Manufacturers and Their Product Offerings. 144
Table 72. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings. 145
Table 73. Neuromorphic Software and Framework Providers and Their Solutions. 146
Table 74. Key Research Institutions and Academia in Neuromorphic Computing and Sensing. 146
Table 75. Competing Emerging Technologies for Neuromorphic Computing and Sensing. 149
Table 76. Technology Substitution Analysis. 153
Table 77. Comparative Advantages/Disadvantages. 155
Table 78. Evolution of Apple Neural Engine. 166
Table 79. Dynex subscription plans. 188

List of Figures

Figure 1. Global market for neuromorphic computing and sensors, 2023-2035 (Millions USD). 19
Figure 2. Neuromorphic Computing and Sensing Market Segmentation 2020-2035. 20
Figure 3. Neuromorphic computing and sensing technology roadmap. 35
Figure 4. Market value chain for neuromorphic computing and sensing. 37
Figure 5. Neuromorphic computing and sensing market map. 38
Figure 6. Evolution of the main hardware technologies for neuromorphic computing. 46
Figure 7. Key materials in NVM technology for neuromorphic computing. 48
Figure 8. Advanced memristive materials for neuromorphic computing. 49
Figure 9. Neural networks in autonomous vehicles. 55
Figure 10. Concept illustration of centralized and decentralized intelligence in robotics. 58
Figure 11. Neuromorphic programmable robot with dynamic vision developed by SynSense. 59
Figure 12. Comparison of High-Level Conventional and Neuromorphic Memory Architectures. 64
Figure 13. Spiking Neural Network (SNN) Structure and Operation. 64
Figure 14. IBM TrueNorth Processor. 71
Figure 15. Event-Based Sensor Operation and Data Processing Flow. 78
Figure 16. Conventional sensor vs. Event-based sensor. 78
Figure 17. Operation of neuromorphic vision sensors. 79
Figure 18. Cyranose 320 Electronic Nose. 80
Figure 19. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information. 82
Figure 20. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications. 92
Figure 21. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD. 97
Figure 22. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation. 98
Figure 23. Sensors used by the ADAS (Advanced Driver-Assistance System). 99
Figure 24. Enabling technologies for autonomous vehicles. 101
Figure 25. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing. 102
Figure 26. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD. 106
Figure 27. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing. 107
Figure 28. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD. 113
Figure 29. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices. 114
Figure 30. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities. 116
Figure 31. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics. 119
Figure 32. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD. 121
Figure 33. Technology roadmap for neuromorphic computing and sensing in aerospace and defense. 122
Figure 34. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight. 123
Figure 35. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD. 129
Figure 36. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services. 130
Figure 37. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD. 136
Figure 38. Global Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD. 138
Figure 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD. 140
Figure 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, Asia-Pacific (2024-2035), millions USD. 141
Figure 41. Global Neuromorphic Computing and Sensing Market Size and Forecast, in in the Rest of the World (2024-2035), millions USD. 142
Figure 42. Neuromorphic Computing and Sensing Ecosystem Overview. 143
Figure 43. Cerebas WSE-2. 179
Figure 44. DeepX NPU DX-GEN1. 187
Figure 45. Google TPU. 195
Figure 46. GrAI VIP. 197
Figure 47. Groq Tensor Streaming Processor (TSP). 199
Figure 48. DVL-5000 neuromorphic laser profiler. 205
Figure 49. Spiking Neural Processor 207
Figure 50. TROOPER robot. 208
Figure 51. 11th Gen Intel® Core™ S-Series. 210
Figure 52. Intel Loihi 2 chip. 210
Figure 53. Envise. 216
Figure 54. Pentonic 2000. 219
Figure 55. Azure Maia 100 and Cobalt 100 chips. 223
Figure 56. Mythic MP10304 Quad-AMP PCIe Card. 226
Figure 57. Nvidia H200 AI chip. 235
Figure 58. Grace Hopper Superchip. 236
Figure 59. Prophesee Metavision starter kit – AMD Kria KV260 and active marker LED board. 243
Figure 60. Cloud AI 100. 245
Figure 61. Overview of SpiNNaker2 architecture for the ”SpiNNcloud” cloud system and edge systems. 261
Figure 62. Untether AI chip. 271

The Global Neuromorphic Computing & Sensing Market 2025-2035

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