The Global Neuromorphic Computing & Sensing Market
報告摘要
The Global Neuromorphic Computing and Sensing Market represents one of the most transformative frontiers in semiconductor development, emerging as the "third stream" alongside traditional digital and quantum computing paradigms. This brain-inspired technology processes information through architectures that fundamentally depart from conventional von Neumann designs, co-locating memory and processing units to eliminate the energy-intensive data shuttling that limits traditional CPU and GPU performance. According to the International Energy Agency, data centres could consume 3% of global electricity by 2030, primarily driven by the computational demands of simulating neural networks. Neuromorphic computing directly addresses this sustainability challenge by implementing neural networks in hardware rather than simulating them through binary sequences. Intel's Loihi 2 neuromorphic processor has demonstrated energy savings of up to 100× over conventional processors for certain inference tasks, while BrainChip's Akida Pulsar delivers 500× lower energy consumption compared to traditional AI cores.
The competitive landscape features a diverse ecosystem spanning established technology giants and innovative startups. Intel's Hala Point system, deployed at Sandia National Laboratories in 2024, represents the world's largest neuromorphic platform with 1.15 billion neurons across 1,152 Loihi 2 processors. IBM's foundational TrueNorth technology continues advancing through neurosynaptic research, while BrainChip has achieved commercial deployment of its Akida processor in millions of IoT devices globally. European players are accelerating through initiatives like the UK Multidisciplinary Centre for Neuromorphic Computing, while Chinese companies including SynSense and Huawei are driving significant IoT and smart city applications.
Key application verticals driving adoption include edge AI and IoT, where neuromorphic chips enable smart sensors, drones, and autonomous vehicles to make real-time decisions with minimal power consumption. Healthcare applications span portable diagnostic devices, wearable monitors detecting cardiac anomalies, and brain-computer interfaces enabling more seamless human-machine communication. Cybersecurity represents an area of immediate commercial viability, with neuromorphic systems excelling at detecting subtle anomalies in network traffic. Financial services benefit from high-frequency trading analysis and fraud detection in complex data streams, while industrial applications encompass predictive maintenance, quality inspection, and supply chain optimization.
Despite promising growth, the market faces meaningful challenges including scalability constraints, integration complexities with existing infrastructure, and the need for standardised programming frameworks. The software ecosystem remains underdeveloped compared to conventional computing, and developing algorithms optimised for neuromorphic hardware requires fundamentally new approaches. However, advances in digital neuromorphic designs replacing analog implementations, alongside standardisation efforts like the Neuromorphic Intermediate Representation, are progressively addressing these barriers.
The convergence of exploding AI workloads, edge device proliferation, and growing energy sustainability requirements positions neuromorphic computing at a critical inflection point. As the technology transitions from research laboratories to commercial products, its potential to enable more intelligent, adaptive, and energy-efficient computation suggests neuromorphic systems will play an increasingly central role in the evolving AI landscape through 2035 and beyond.
The Global Neuromorphic Computing & Sensing Market 2026-2036 provides comprehensive analysis of the rapidly evolving brain-inspired computing industry, now recognized as the "third stream" of semiconductor development alongside digital and quantum technologies. This definitive market intelligence report delivers actionable insights for investors, technology strategists, and industry stakeholders seeking to capitalize on one of the fastest-growing segments in artificial intelligence hardware.
Neuromorphic computing represents a paradigm shift in how machines process information, drawing direct inspiration from biological neural networks to achieve unprecedented energy efficiency and real-time processing capabilities. With data centres projected to consume 3% of global electricity by 2030 due to conventional AI workloads, neuromorphic technology offers a sustainable pathway forward. This extensively researched report examines the complete neuromorphic ecosystem spanning hardware, software, sensors, and applications. The analysis covers spiking neural networks, emerging non-volatile memory technologies including Phase-Change Memory, Resistive RAM, Magnetoresistive RAM, and Ferroelectric RAM, alongside detailed assessment of digital, analog, and mixed-signal neuromorphic processor architectures.
The report delivers granular market forecasts segmented by technology type, application vertical, and geographic region through 2036. Key application sectors analyzed include mobile and consumer electronics, automotive and transportation, industrial manufacturing, healthcare and medical devices, aerospace and defense, and datacenter infrastructure. Regional analysis encompasses North America, Europe, Asia-Pacific, and Rest of World markets with country-level insights.
Strategic business intelligence includes comprehensive competitive landscape analysis, funding and investment tracking, merger and acquisition activity, and partnership developments shaping industry dynamics. The report profiles 149 companies across the neuromorphic value chain, from semiconductor giants to innovative startups pioneering brain-inspired computing solutions.
Market drivers analyzed include the unsustainable energy trajectory of conventional AI, proliferating edge device deployments, autonomous vehicle development, and breakthrough achievements in commercial neuromorphic hardware. Challenges addressed encompass the programming paradigm gap, manufacturing scalability, software ecosystem fragmentation, and developer talent shortages, with resolution timelines projected through 2036.
The report provides technology roadmaps spanning near-term commercialization through long-term research horizons, enabling strategic planning for product development, investment timing, and market entry decisions. Comparative analysis positions neuromorphic computing against competing emerging technologies including quantum computing, photonic computing, and analog AI chips.
Applications spanning autonomous vehicles, humanoid robotics, brain-computer interfaces, cybersecurity, and energy-efficient data centres are driving adoption across industries. This report serves technology executives, venture capital investors, corporate strategists, semiconductor manufacturers, system integrators, and government policymakers requiring authoritative market intelligence on neuromorphic computing and sensing technologies. The analysis synthesizes primary research, company disclosures, patent analysis, and expert interviews to deliver the most comprehensive assessment of this transformative market available.
Report Contents Include:
Global market revenues and forecasts 2024-2036
Market segmentation by technology, application, and region
Key market trends, growth drivers, and challenges
Industry insights on digital vs. analog implementations
Technology roadmap and future outlook
Key product launches 2024-2025
Funding, investments, and M&A activity
Regulatory and ethical considerations
Sustainability and environmental impact analysis
Technology Deep-Dive
Spiking Neural Networks (SNNs) architecture and principles
Memory technologies: SRAM, DRAM, PCM, RRAM, MRAM, FeRAM
In-memory and near-memory computing approaches
Neuromorphic hardware: digital, analog, mixed-signal, FPGA-based processors
Software frameworks, programming tools, and SDKs
Algorithm libraries and simulation platforms
Neuromorphic Sensing Technologies
Event-based vision, auditory, and olfactory sensors
Hybrid sensing approaches and multi-modal fusion
Pixel-level processing and sensor-processor co-design
Signal processing and feature extraction techniques
Spike-based encoding and temporal feature extraction
Application Market Analysis & Forecasts
Mobile and consumer applications
Automotive and transportation (ADAS, autonomous vehicles)
Industrial IoT and smart manufacturing
Healthcare and medical devices
Aerospace and defense
Datacenters and cloud services
Commercial deployment case studies
Regional Market Analysis
North America market size and forecasts
Europe market dynamics and key initiatives
Asia-Pacific growth drivers and opportunities
Rest of World emerging markets
Regional development highlights and government initiatives
Competitive Landscape
Neuromorphic chip manufacturers
Sensor manufacturers
Emerging NVM manufacturers
Software and framework providers
Research institutions and academia
Competing emerging technologies analysis
Technology substitution and migration pathways
151 Company Profiles
Business overview and product portfolios
Technology capabilities and roadmaps
Strategic partnerships and funding
Market positioning and competitive advantages
This report features detailed profiles of 151 leading companies shaping the neuromorphic computing and sensing industry: ABR (Applied Brain Research), AiM Future, AI Startek, AI Storm, AlpsenTek, Amazon Web Services (AWS), Ambarella, Ambient Scientific, Advanced Micro Devices (AMD), ANAFLASH, Analog Inference, AnotherBrain, Apple, ARM, Aryballe Technologies, Aspinity, Aspirare Semi, Avalanche Technology, Axelera AI, Baidu Inc., Beijing Xinzhida Neurotechnology, Blumind Inc., BMW, Bosch, BrainChip, Canon, CEA-Leti, Celepixel, Celestial AI, Cerebras Systems, Ceryx Medical, Ceva Inc., ChipIntelli, Clarifai, CoCoPIE, Cognifiber, Crossbar Inc., d-Matrix, DeepLite, DeepX, Dialog Semiconductor, Dynex, EdgeCortix, Eeasy Technology, Evomotion, Expedera, Fullhan, General Vision, GlobalFoundries, Google, Gorilla Technology, GrAI Matter Labs, Green Mountain Semiconductor, Grayscale AI, Groq, Gwanak Analog Co. Ltd., Hailo, HPLabs, Hikvision, Huawei, IBM, Infineon Technologies AG, iniVation AG, Innatera Nanosystems B.V., Instar-Robotics, Intel, Intelligent Hardware Korea (IHWK), Intrinsic Semiconductor Technologies, Kalray SA, KIST (Korea Institute of Science and Technology), Koniku, Kneron, Knowm, Lightmatter, Lumai, Lynxi Technology, MatX, MediaTek, MemComputing Inc., MemryX, Mentium Technologies, Meta, Microsoft, Mindtrace, Moffett AI, Mythic, MythWorx and more
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目錄 Table of Contents
1 EXECUTIVE SUMMARY 18
1.1 Overview of the neuromorphic computing and sensing market 18
1.1.1 Market Performance 2024-2025 19
1.1.2 Revised Long-Term Projections Through 2036 19
1.1.3 Global Market Revenues 2024-2036 20
1.1.4 Market segmentation 21
1.2 Ending of Moore’s Law 22
1.3 Historical market 23
1.4 The market in 2024 24
1.5 Key market trends and growth drivers 24
1.6 Market challenges and limitations 25
1.7 Key Industry Insights 26
1.7.1 Digital Neuromorphic Designs Replacing Analog Implementations 26
1.7.2 The Programming Paradigm Gap 27
1.7.3 Cost and Scalability Considerations 27
1.8 Future outlook and opportunities 28
1.8.1 Emerging trends 28
1.8.1.1 Hybrid Neuromorphic-Conventional Computing and Sensing Systems 29
1.8.1.2 Edge AI and IoT 30
1.8.1.3 Quantum Computing 31
1.8.1.4 Explainable AI 31
1.8.1.5 Brain-Computer Interfaces 32
1.8.1.6 Energy-efficient AI at scale 32
1.8.1.7 Real-time learning and adaptation 33
1.8.1.8 Enhanced Perception Systems 33
1.8.1.9 Large-scale Neuroscience Simulations 34
1.8.1.10 Secure, Decentralized AI 34
1.8.1.11 Robotics that mimic humans 34
1.8.1.12 Neural implants for healthcare 35
1.8.1.13 New Application Areas and Use Cases 35
1.8.1.14 Disruptive Business Models and Services 36
1.8.1.15 Collaborative Ecosystem Development 36
1.8.1.16 Skill Development and Workforce Training 36
1.8.2 Technology roadmap 38
1.9 Key Product Launches (2024-2025) 39
1.9.1 Intel Hala Point System (April 2024) 39
1.9.2 Innatera T1 SNP Processor (CES 2025) 40
1.9.3 BrainChip Akida Pulsar 40
1.9.4 BrainChip Akida Cloud (August 2025) 40
1.9.5 SynSense Speck 2.0 40
1.9.6 Chinese Academy of Sciences SpikingBrain-1.0 40
1.10 Neuromorphic computing and generative AI 41
1.11 Market value chain 42
1.12 Market map 45
1.13 Funding and investments 47
1.14 Strategic Partnerships and Collaborations 50
1.15 Regulatory and Ethical Considerations 55
1.15.1 Data Privacy and Security 55
1.15.2 Bias and Fairness in Neuromorphic Systems 55
1.15.3 Intellectual Property and Patent Landscape 55
1.16 Sustainability and Environmental Impact 56
1.16.1 Carbon Footprint Analysis of Neuromorphic Systems 56
1.16.2 Energy Efficiency Metrics and Benchmarking 57
1.16.3 Green Manufacturing Practices 57
1.16.4 End-of-life and Recycling Considerations 57
1.16.5 Environmental Regulations Compliance 57
2 INTRODUCTION 59
2.1 Definition and concept of neuromorphic computing and sensing 59
2.2 Main neuromorphic approaches 60
2.2.1 Large-scale hardware neuromorphic computing systems 62
2.2.2 Non-volatile memory technologies 62
2.2.3 Advanced memristive materials and devices 63
2.3 Fabrication Processes for Neuromorphic Systems 64
2.4 Key Material Suppliers 64
2.5 Supply Chain Vulnerabilities and Mitigation 65
2.6 Manufacturing Capacity Analysis 65
2.7 Quality Control and Testing Procedures 66
2.8 Comparison with traditional computing and sensing approaches 66
2.9 Neuromorphic computing vs. quantum computing 67
2.10 Key features and advantages 68
2.10.1 Low latency and real-time processing 68
2.10.2 Power efficiency and energy savings 68
2.10.3 Scalability and adaptability 69
2.10.4 Online learning and autonomous decision-making 69
2.11 Markets and Applications 69
2.11.1 Edge AI and IoT 73
2.11.2 Autonomous Vehicles and Robotics 74
2.11.3 Cybersecurity and Anomaly Detection 76
2.11.4 Smart Sensors and Monitoring Systems 77
2.11.5 Datacenter and High-Performance Computing 78
3 NEUROMORPHIC COMPUTING TECHNOLOGIES AND ARCHITECTURE 80
3.1 Spiking Neural Networks (SNNs) 81
3.1.1 Biological inspiration and principles 82
3.1.2 Types of SNNs and their characteristics 82
3.1.3 Advantages and limitations of SNNs 82
3.2 Memory Architectures for Neuromorphic Computing 83
3.2.1 Conventional memory approaches (SRAM, DRAM) 83
3.2.2 Emerging non-volatile memory (eNVM) technologies 84
3.2.2.1 Phase-Change Memory (PCM) 84
3.2.2.2 Resistive RAM (RRAM) 84
3.2.2.3 Magnetoresistive RAM (MRAM) 85
3.2.2.4 Ferroelectric RAM (FeRAM) 85
3.2.3 In-memory computing and near-memory computing 86
3.2.4 Hybrid memory architectures 86
3.3 Neuromorphic Hardware and Processors 87
3.3.1 Digital neuromorphic processors 87
3.3.2 Analog neuromorphic processors 88
3.3.3 Mixed-signal neuromorphic processors 88
3.3.4 FPGA-based neuromorphic systems 89
3.3.5 Neuromorphic accelerators and co-processors 89
3.4 Software and Frameworks for Neuromorphic Computing 90
3.4.1 Neuromorphic programming languages and tools 90
3.4.2 Neuromorphic simulation platforms and frameworks 91
3.4.3 Software and Programming Ecosystem Developments 92
3.4.3.1 Intel Lava Framework 92
3.4.3.2 Neuromorphic Intermediate Representation (NIR) 92
3.4.3.3 PyTorch-Based SNN Libraries 92
3.4.3.4 Nengo Cross-Platform Framework 93
3.4.4 Neuromorphic algorithm libraries and repositories 93
3.4.5 Neuromorphic software development kits (SDKs) 94
4 NEUROMORPHIC SENSING TECHNOLOGIES AND ARCHITECTURES 96
4.1 Event-Based Sensors and Processing 96
4.1.1 Neuromorphic vision sensors 97
4.1.2 Neuromorphic auditory sensors 98
4.1.3 Neuromorphic olfactory sensors 99
4.1.4 Event-driven processing and algorithms 100
4.2 Hybrid Sensing Approaches 100
4.2.1 Combination of conventional and event-based sensors 102
4.2.2 Fusion of multiple sensing modalities 102
4.2.3 Advantages and challenges of hybrid sensing 103
4.3 Neuromorphic Sensor Architectures and Designs 104
4.3.1 Pixel-level processing and computation 104
4.3.2 Sensor-processor co-design and integration 104
4.3.3 Bio-inspired sensor designs and materials 105
4.4 Signal Processing and Feature Extraction Techniques 106
4.4.1 Spike-based Encoding and Decoding 107
4.4.2 Temporal and Spatiotemporal Feature Extraction 107
4.4.3 Neuromorphic Filtering and Denoising 108
4.4.4 Adaptive and Learning-Based Processing 109
5 MARKET ANALYSIS AND FORECASTS 111
5.1 Commercial Deployment Highlights 2025 111
5.1.1 IoT and Edge Deployments 111
5.1.2 Automotive Applications: Mercedes-Benz 111
5.1.3 Telecommunications: Ericsson Research 111
5.1.4 Healthcare: ALYN Hospital Collaboration 111
5.1.5 Cybersecurity Applications 112
5.2 Mobile and Consumer Applications 113
5.2.1 Smartphones and wearables 113
5.2.2 Smart home and IoT devices 114
5.2.3 Consumer health and wellness 115
5.2.4 Entertainment and gaming 116
5.3 Automotive and Transportation 119
5.3.1 Advanced Driver Assistance Systems (ADAS) 119
5.3.2 Autonomous vehicles and robotaxis 121
5.3.3 Vehicle infotainment and user experience 124
5.3.4 Smart traffic management and infrastructure 125
5.4 Industrial and Manufacturing 128
5.4.1 Industrial IoT and smart factories 129
5.4.2 Predictive maintenance and anomaly detection 130
5.4.3 Quality control and inspection 131
5.4.4 Logistics and supply chain optimization 132
5.5 Healthcare and Medical Devices 135
5.5.1 Medical imaging and diagnostics 136
5.5.2 Wearable health monitoring devices 137
5.5.3 Personalized medicine and drug discovery 138
5.5.4 Assistive technologies and prosthetics 140
5.6 Aerospace and Defense 143
5.6.1 Unmanned Aerial Vehicles (UAVs) and drones 144
5.6.2 Satellite imaging and remote sensing 145
5.6.3 Missile guidance and target recognition 146
5.6.4 Cybersecurity and threat detection: 148
5.7 Datacenters and Cloud Services 151
5.7.1 High-performance computing and scientific simulations: 151
5.7.2 Big data analytics and machine learning 153
5.7.3 Cloud-based AI services and platforms 154
5.7.4 Energy-efficient datacenter infrastructure 155
5.8 Regional Market Analysis and Forecasts 158
5.8.1 North America 159
5.8.2 Europe 160
5.8.3 Asia-Pacific 162
5.8.4 Rest of the World 163
5.9 Competitive Landscape and Key Players 165
5.9.1 Overview of the Neuromorphic Computing and Sensing Ecosystem 165
5.9.2 Neuromorphic Chip Manufacturers and Processors 165
5.9.3 Neuromorphic Sensor Manufacturers 166
5.9.4 Emerging Non-Volatile Memory (eNVM) Manufacturers 167
5.9.5 Neuromorphic Software and Framework Providers 167
5.9.6 Research Institutions and Academia 168
5.10 Competing Emerging Technologies 171
5.10.1 Quantum Computing 171
5.10.2 Photonic Computing 172
5.10.3 DNA Computing 172
5.10.4 Spintronic Computing 172
5.10.5 Chemical Computing 172
5.10.6 Superconducting Computing 173
5.10.7 Analog AI Chips 173
5.10.8 In-Memory Computing 173
5.10.9 Reversible Computing 173
5.10.10 Quantum Dot Computing 174
5.10.11 Technology Substitution Analysis 174
5.10.12 Migration Pathways 176
5.10.13 Comparative Advantages/Disadvantages 177
6 COMPANY PROFILES 178 (151 company profiles)
7 RESEARCH SCOPE & METHODOLOGY 361
8 REFERENCES 362
圖表清單 List of Tables & Figures
List of Tables
Table 1. Overview of the neuromorphic computing and sensing market. 18
Table 2. Global market for neuromorphic computing and sensors, 2024-2036 (Billions USD). 20
Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2036. 21
Table 4. Key market trends and growth drivers. 25
Table 5. Market challenges and limitations. 26
Table 6. Emerging Trends in Neuromorphic Computing and Sensing 28
Table 7. Key Product Launches. 41
Table 8. Neuromorphic computing and generative AI strategies. 42
Table 9. Funding and investments in neuromorphic computing and sensing. 47
Table 10. Strategic Partnerships and Collaborations in the Neuromorphic Industry. 50
Table 11. Regulatory and Ethical Considerations of neuromorphic computing & sensing. 56
Table 12. Main neuromorphic sensing approaches. 60
Table 13. Main Neuromorphic Computing Approaches. 60
Table 14. Resistive Non-Volatile Memory (NVM) Technologies. 62
Table 15. Advanced Memristive Materials, Devices, and Novel Computation Concepts. 63
Table 16. Fabrication Processes for Neuromorphic Systems. 64
Table 17. Key Material Suppliers and Dependencies. 64
Table 18. Comparison with traditional computing and sensing approaches. 66
Table 19. Comparison between neuromorphic and quantum computing. 67
Table 20. Key features and advantages of neuromorphic computing and sensing. 68
Table 21. Markets and Applications of Neuromorphic Computing and Sensing 70
Table 22. Von neumann architecture versus neuromorphic architecture. 80
Table 23. Types of SNNs and their characteristics. 82
Table 24. Advantages and limitations of SNNs. 83
Table 25. Conventional memory approaches (SRAM, DRAM). 83
Table 26. Emerging non-volatile memory (eNVM) technologies. 84
Table 27. Hybrid memory architectures. 87
Table 28. Neuromorphic accelerators and co-processors. 90
Table 29. Neuromorphic programming languages and tools. 91
Table 30. Neuromorphic simulation platforms and frameworks. 91
Table 31. Neuromorphic Software Ecosystem 2025-2036 93
Table 32. Software Ecosystem Maturity Timeline 93
Table 33. Neuromorphic algorithm libraries and repositories. 94
Table 34. Neuromorphic software development kits (SDKs). 95
Table 35. Hybrid sensing approaches. 101
Table 36. Advantages and challenges of hybrid sensing. 103
Table 37. Bio-inspired sensor designs and materials. 105
Table 38. Signal Processing and Feature Extraction Techniques. 106
Table 39. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application. 114
Table 40. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application. 114
Table 41. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application. 115
Table 42. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application. 117
Table 43. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD. 117
Table 44. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application. 120
Table 45. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application. 123
Table 46. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application. 125
Table 47. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application. 126
Table 48. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD. 126
Table 49. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application. 129
Table 50. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application. 131
Table 51. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application. 132
Table 52. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application. 133
Table 53. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD. 134
Table 54. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application. 136
Table 55. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application. 138
Table 56. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application. 139
Table 57. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application. 141
Table 58. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD. 141
Table 59. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application. 144
Table 60. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application. 146
Table 61. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application. 147
Table 62. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application. 148
Table 63. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD. 149
Table 64. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application. 152
Table 65. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application. 153
Table 66. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application. 154
Table 67. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application. 156
Table 68. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD. 157
Table 69. Market revenues for neuromorphic computing and sensing by region from 2024-2036 in millions USD. 158
Table 70. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2036), millions USD. 159
Table 71. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2036), millions USD. 161
Table 72. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2036), millions USD. 162
Table 73. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2036), millions USD. 164
Table 74. Neuromorphic Chip Manufacturers and Their Product Offerings. 166
Table 75. Neuromorphic Sensor Manufacturers and Their Product Offerings. 166
Table 76. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings. 167
Table 77. Neuromorphic Software and Framework Providers and Their Solutions. 168
Table 78. Key Research Institutions and Academia in Neuromorphic Computing and Sensing. 168
Table 79. Competing Emerging Technologies for Neuromorphic Computing and Sensing. 171
Table 80. Technology Substitution Analysis. 175
Table 81. Comparative Advantages/Disadvantages. 177
Table 82. Evolution of Apple Neural Engine. 191
Table 83. Dynex subscription plans. 225
List of Figures
Figure 1. Global market for neuromorphic computing and sensors, 2023-2036 (Millions USD). 21
Figure 2. Neuromorphic computing and sensing technology roadmap. 39
Figure 3. Market value chain for neuromorphic computing and sensing. 43
Figure 4. Neuromorphic computing and sensing market map. 46
Figure 5. Evolution of the main hardware technologies for neuromorphic computing. 61
Figure 6. Key materials in NVM technology for neuromorphic computing. 63
Figure 7. Advanced memristive materials for neuromorphic computing. 64
Figure 8. Neural networks in autonomous vehicles. 72
Figure 9. Concept illustration of centralized and decentralized intelligence in robotics. 75
Figure 10. Neuromorphic programmable robot with dynamic vision developed by SynSense. 76
Figure 11. Comparison of High-Level Conventional and Neuromorphic Memory Architectures. 81
Figure 12. Spiking Neural Network (SNN) Structure and Operation. 81
Figure 13. IBM TrueNorth Processor. 88
Figure 14. Event-Based Sensor Operation and Data Processing Flow. 96
Figure 15. Conventional sensor vs. Event-based sensor. 97
Figure 16. Operation of neuromorphic vision sensors. 98
Figure 17. Cyranose 320 Electronic Nose. 99
Figure 18. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information. 101
Figure 19. Commercial Application Milestones 2024-2025 112
Figure 20. Application Market Penetration Timeline 112
Figure 21. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications. 113
Figure 22. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD. 118
Figure 23. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation. 119
Figure 24. Sensors used by the ADAS (Advanced Driver-Assistance System). 120
Figure 25. Enabling technologies for autonomous vehicles. 122
Figure 26. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing. 123
Figure 27. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD. 127
Figure 28. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing. 128
Figure 29. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD. 134
Figure 30. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices. 135
Figure 31. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities. 137
Figure 32. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics. 140
Figure 33. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD. 142
Figure 34. Technology roadmap for neuromorphic computing and sensing in aerospace and defense. 143
Figure 35. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight. 144
Figure 36. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD. 150
Figure 37. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services. 151
Figure 38. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD. 158
Figure 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2036), millions USD. 160
Figure 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2036), millions USD. 161
Figure 41. Global Neuromorphic Computing and Sensing Market Size and Forecast, Asia-Pacific (2024-2036), millions USD. 163
Figure 42. Global Neuromorphic Computing and Sensing Market Size and Forecast, in in the Rest of the World (2024-2036), millions USD. 164
Figure 43. Neuromorphic Computing and Sensing Ecosystem Overview. 165
Figure 44. Cerebas WSE-2. 212
Figure 45. DeepX NPU DX-GEN1. 223
Figure 46. Google TPU. 233
Figure 47. GrAI VIP. 235
Figure 48. Groq Tensor Streaming Processor (TSP). 239
Figure 49. DVL-5000 neuromorphic laser profiler. 248
Figure 50. Spiking Neural Processor 250
Figure 51. TROOPER robot. 252
Figure 52. 11th Gen Intel® Core™ S-Series. 254
Figure 53. Intel Loihi 2 chip. 254
Figure 54. Envise. 263
Figure 55. Pentonic 2000. 267
Figure 56. Azure Maia 100 and Cobalt 100 chips. 273
Figure 57. Mythic MP10304 Quad-AMP PCIe Card. 277
Figure 58. Nvidia H200 AI chip. 293
Figure 59. Grace Hopper Superchip. 294
Figure 60. Prophesee Metavision starter kit – AMD Kria KV260 and active marker LED board. 307
Figure 61. Cloud AI 100. 309
Figure 62. Overview of SpiNNaker2 architecture for the ”SpiNNcloud” cloud system and edge systems. 333
Figure 63. Untether AI chip. 349
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