Neuromorphic computing and sensing had its most active year yet in 2026, as investors, chipmakers and system builders moved from research toward commercial deployment of brain-inspired hardware. Interest was driven by the energy cost of conventional AI. Neuromorphic processors, in-memory compute and event-based sensors process information only when something changes, so they offer large efficiency gains for edge devices, robotics, vehicles and, increasingly, the data centre. Private capital moved into the sector at unprecedented scale:

• Flourish raised US$500 million in June 2026, in a round led by Jeff Bezos, to reverse-engineer how the brain learns.
• Unconventional AI, led by Naveen Rao, closed a US$475 million seed round at a US$4.5 billion valuation in late 2025 to develop analogue, physics-based AI chips.
• Neurophos raised a US$110 million Series A for optical processing.
• Axelera AI secured more than US$250 million.
• MatX closed a Series B of over US$500 million.
• Salience Labs raised a US$125 million Series B in October.

Europe’s memristor developers also gained momentum. Romania’s CyberSwarm closed a US$50 million Series A led by Falanga Invest Perton to take its memristor-based compute architecture into automotive, aerospace, defence and robotics. The Biological Computing Co. raised a US$25 million seed round to build computers that use living neurons. Applied Brain Research completed an oversubscribed seed round, and Prophesee secured €20 million of new financing after its restructuring.

In Asia:

• SynSense closed a Series B.
• AlpsenTek raised a Series B+ for hybrid event-based vision sensors.
• Moffett AI announced a Series C of nearly CNY1 billion.
• DEEPX began a Series D valuing it at about US$2.2 billion.
• Rebellions, which absorbed Sapeon, raised US$400 million ahead of an IPO.

Strategic activity was just as significant. Marvell completed its acquisition of Celestial AI, Honda partnered with and invested in Mythic to co-develop a neuromorphic chip for vehicles, and Capacity acquired Starmind. Commercial milestones followed the money:

• BrainChip began shipping AKD1500 chips in volume and licensed its Akida IP to new chip customers.
• Innatera’s Pulsar microcontroller won its first manufacturing customer.
• TetraMem announced first silicon of its MLX200 in-memory compute chip.
• SpiNNcloud delivered large-scale SpiNNaker2 systems to research centres.

Together, these developments mark 2026 as the year neuromorphic technology attracted mainstream venture and corporate capital. The field’s focus has shifted from proving the concept to scaling production, software ecosystems and real-world deployments.

The Global Neuromorphic Computing & Sensing Market 2027-2037 is a comprehensive analysis of this fast-evolving industry. It covers the technologies, materials, manufacturing processes, end markets and companies shaping it. The report gives revenue forecasts to 2037 by end market and region, with conservative, moderate and aggressive scenarios. It also assesses the commercial readiness of each technology and company, giving investors, chipmakers, OEMs and system integrators a clear view of where and when commercial adoption will happen.

Report contents include:

• Executive summary of market performance from 2024 to 2026, revised long-term projections, key trends, growth drivers, challenges and the technology roadmap to 2037.
• Key product launches, system deployments and 2026 funding activity, including venture, corporate and government investment.
• Market value chain, market map, strategic partnerships, and the regulatory, intellectual property and sustainability landscape.
• Neuromorphic approaches, including large-scale systems, non-volatile memory, and memristive materials and devices.
• Fabrication processes, key material suppliers, supply-chain risks and manufacturing capacity.
• Neuromorphic computing technologies, including spiking neural networks, memory architectures, processors and software frameworks.
• Neuromorphic sensing technologies, including event-based vision, auditory, olfactory and tactile sensors, hybrid sensing approaches and signal processing.
• Comparison with conventional, quantum and other emerging computing technologies.
• Market forecasts by end market to 2037 for mobile and consumer, automotive and transportation, industrial and manufacturing, healthcare and medical devices, aerospace and defence, and data centres and cloud services.
• Regional forecasts for North America, Europe, Asia-Pacific and the Rest of the World.
• Competitive landscape and analysis of competing technologies.
• Profiles of 154 companies, each with technology, products, funding history to October 2026, recent developments, a commercial-readiness assessment and the Future Markets strategic view.
• Convergence with the broader neural networks market, plus research methodology and references.

Companies profiled include Applied Brain Research (ABR), AiM Future, AistarTek, AIStorm, AlpsenTek, Amazon Web Services (AWS), Ambarella, Ambient Scientific, AMD, ANAFLASH, Sagence AI, AnotherBrain, Apple, Arm, Aryballe Technologies, Aspinity, Aspirare Semi, Avalanche Technology, Axelera AI, Baidu, Beijing Xinzhida Neurotechnology, The Biological Computing Co., Blumind, BMW, Bosch, BrainChip, Canon, CEA-Leti, CelePixel, Celestial AI, Cerebras Systems, Ceryx Medical, Ceva, ChipIntelli, Clarifai, CoCoPIE, CogniFiber, CrossBar, CyberSwarm, d-Matrix, Deeplite, DEEPX, Dialog Semiconductor, Dynex, EdgeCortix, Eeasy Technology, Evomotion, Expedera, Flourish, Fullhan Microelectronics, General Vision, GlobalFoundries, Google, Gorilla Technology, GrAI Matter Labs, Green Mountain Semiconductor, Grayscale AI, Groq, Gwanak Analog, Hailo, Hewlett Packard Labs, Hikvision, Huawei, IBM, Infineon Technologies, iniVation, Innatera Nanosystems, INSTAR Robotics, Intel, Intelligent Hardware Korea, Intrinsic Semiconductor Technologies and more.....

1. Executive Summary 54

• 1.1 Overview of the Neuromorphic Computing and Sensing Market         54
  • 1.1.1 Market Performance 2024-2026          56
  • 1.1.2 Revised Long-Term Projections Through 2037            56
  • 1.1.3 Global Market Revenues 2024-2037 56
  • 1.1.4 Market Segmentation  57
• 1.2 Ending of Moore’s Law     58
• 1.3 Historical Market 59
• 1.4 The Market in 2026            60
• 1.5 Key Market Trends and Growth Drivers 61
• 1.6 Market Challenges and Limitations        62
• 1.7 System Deployments       63
• 1.8 Key Industry Insights        64
• 1.8.1 Digital Neuromorphic Designs Replacing Analog Implementations             64
• 1.8.2 The Programming Paradigm Gap         64
• 1.8.3 Cost and Scalability Considerations 64
• 1.9.1 Emerging Trends             65
• 1.9.2 Technology Roadmap 75
• 1.10.1 Intel Hala Point System (April 2024) 76
• 1.10.2 Innatera T1 SNP Processor (CES 2025)         76
• 1.10.3 BrainChip Akida Pulsar            77
• 1.10.4 BrainChip Akida Cloud (August 2025)           77
• 1.10.5 SynSense Speck 2.0  77
• 1.10.6 Chinese Academy of Sciences SpikingBrain-1.0    77
• 1.10.7 Flourish AI $500M Private Round (2026)      78
• 1.10.8 Unconventional AI $475M Seed Round (2025)        78
• 1.10.9 CyberSwarm Series A and Memristor Chip Sampling (2026)          78
• 1.9 Future Outlook and Opportunities          65
• 1.10 Key Product Launches (2024-2026)    76
• 1.11 Neuromorphic Computing and Generative AI               82
• 1.14.1 Government & Institutional Funding               85
• 1.14.2 Venture Capital & Private Equity Flows          85
• 1.12 Market Value Chain        83
• 1.16.1 Data Privacy and Security       95
• 1.16.2 Bias and Fairness in Neuromorphic Systems            95
• 1.16.3 Intellectual Property and Patent Landscape             96
• 1.17.1 Carbon Footprint Analysis of Neuromorphic Systems        97
• 1.17.2 Energy Efficiency Metrics and Benchmarking           97
• 1.17.3 Green Manufacturing Practices         97
• 1.17.4 End-of-life and Recycling Considerations   97
• 1.17.5 Environmental Regulations Compliance     9
• 1.13 Market Map          84
• 1.14 Funding and Investments           84
• 1.15 Strategic Partnerships and Collaborations     90
• 1.16 Regulatory and Ethical Considerations             95
• 1.17 Sustainability and Environmental Impact        97

2. Introduction 99

• 2.1 Definition and Concept of Neuromorphic Computing and Sensing  99
• 2.2 Main Neuromorphic Approaches             100
  • 2.2.1 Large-scale Hardware Neuromorphic Computing Systems               102
  • 2.2.2 Non-volatile Memory Technologies    102
  • 2.2.3 Advanced Memristive Materials and Devices               103
• 2.3 Fabrication Processes for Neuromorphic Systems      104
• 2.4 Key Material Suppliers     107
• 2.5 Supply Chain Vulnerabilities and Mitigation     108
• 2.6 Manufacturing Capacity Analysis            108
• 2.7 Quality Control and Testing Procedures               108
• 2.8 Comparison with Traditional Computing and Sensing Approaches  109
• 2.9 Neuromorphic Computing Vs. Quantum Computing 109
• 2.10 Key Features and Advantages  111
• 2.10.1 Low Latency and Real-time Processing        111
• 2.10.2 Power Efficiency and Energy Savings              111
• 2.10.3 Scalability and Adaptability  112
• 2.10.4 Online Learning and Autonomous Decision-making           112
• 2.11.1 Edge AI and IoT              116
• 2.11.2 Autonomous Vehicles and Robotics               117
• 2.11.3 Cybersecurity and Anomaly Detection          119
• 2.11.4 Smart Sensors and Monitoring Systems      120
• 2.11.5 Datacenter and High-Performance Computing       121
• 2.11 Markets and Applications          112

3. Neuromorphic Computing Technologies and Architecture 123

• 3.1 Spiking Neural Networks (SNNs)              124
  • 3.1.1 Biological Inspiration and Principles  125
  • 3.1.2 Types of SNNs and Their Characteristics        125
  • 3.1.3 Advantages and Limitations of SNNs 126
• 3.2 Memory Architectures for Neuromorphic Computing 127
• 3.2.1 Conventional Memory Approaches (SRAM, DRAM) 127
• 3.2.2 Emerging Non-volatile Memory (eNVM) Technologies            127
• 3.2.3 In-memory Computing and Near-memory Computing         130
• 3.2.4 Hybrid Memory Architectures 130
• 3.3.1 Digital Neuromorphic Processors       131
• 3.3.2 Analog Neuromorphic Processors      132
• 3.3.3 Mixed-signal Neuromorphic Processors         132
• 3.3.4 FPGA-based Neuromorphic Systems               133
• 3.3.5 Neuromorphic Accelerators and Co-processors       133
• 3.4.1 Neuromorphic Programming Languages and Tools 138
• 3.4.2 Neuromorphic Simulation Platforms and Frameworks         139
• 3.4.3 Software and Programming Ecosystem Developments        139
• 3.4.4 Neuromorphic Algorithm Libraries and Repositories              149
• 3.4.5 Neuromorphic Software Development Kits (SDKs)  149
• 3.3 Neuromorphic Hardware and Processors          131
• 3.4 Software and Frameworks for Neuromorphic Computing       138

4. Neuromorphic Sensing Technologies and Architectures 151

• 4.1 Event-Based Sensors and Processing   151
  • 4.1.1 Neuromorphic Vision Sensors               152
  • 4.1.2 Neuromorphic Auditory Sensors          153
  • 4.1.3 Neuromorphic Olfactory Sensors        154
  • 4.1.4 Event-driven Processing and Algorithms        155
• 4.2 Hybrid Sensing Approaches        155
• 4.2.1 Combination of Conventional and Event-based Sensors    157
• 4.2.2 Fusion of Multiple Sensing Modalities              157
• 4.2.3 Advantages and Challenges of Hybrid Sensing          158
• 4.3.1 Pixel-level Processing and Computation        159
• 4.3.2 Sensor-processor Co-design and Integration              159
• 4.3.3 Bio-inspired Sensor Designs and Materials  160
• 4.4.1 Spike-based Encoding and Decoding               161
• 4.4.2 Temporal and Spatiotemporal Feature Extraction     162
• 4.4.3 Neuromorphic Filtering and Denoising            163
• 4.4.4 Adaptive and Learning-Based Processing      164
• 4.3 Neuromorphic Sensor Architectures and Designs       158
• 4.4 Signal Processing and Feature Extraction Techniques                161

5. Market Analysis and Forecasts 166

• 5.1 Commercial Deployment Highlights     166
  • 5.1.1 IoT and Edge Deployments       166
  • 5.1.2 Automotive Applications: Mercedes-Benz     166
  • 5.1.3 Telecommunications: Ericsson Research      166
  • 5.1.4 Healthcare: ALYN Hospital Collaboration      166
  • 5.1.5 Cybersecurity Applications     167
• 5.2 Mobile and Consumer     169
• 5.2.1 Applications      169
• 5.2.2 Technology Roadmap 170
• 5.2.3 Smartphones and Wearables 171
• 5.2.4 Smart Home and IoT Devices  172
• 5.2.5 Consumer Health and Wellness           173
• 5.2.6 Entertainment and Gaming     174
• 5.2.7 Market Size and Forecast, in Mobile and Consumer Applications (2024-2037), Millions USD.   175
• 5.3.1 Applications      176
• 5.3.2 Technology Roadmap 177
• 5.3.3 Advanced Driver Assistance Systems (ADAS)              178
• 5.3.4 Autonomous Vehicles and Robotaxis               180
• 5.3.5 Vehicle Infotainment and User Experience    183
• 5.3.6 Smart Traffic Management and Infrastructure             184
• 5.3.7 Market Size and Forecast, in Automotive and Transportation (2024-2037), Millions USD.             185
• 5.4.1 Applications      187
• 5.4.2 Technology Roadmap 188
• 5.4.3 Industrial IoT and Smart Factories       188
• 5.4.4 Predictive Maintenance and Anomaly Detection       190
• 5.4.5 Quality Control and Inspection             191
• 5.4.6 Logistics and Supply Chain Optimization      192
• 5.4.7 Market Size and Forecast, in Industrial and Manufacturing (2024-2037), Millions USD. 193
• 5.5.1 Applications      195
• 5.5.2 Technology Roadmap 196
• 5.5.3 Medical Imaging and Diagnostics       197
• 5.5.4 Wearable Health Monitoring Devices                198
• 5.5.5 Personalized Medicine and Drug Discovery  200
• 5.5.6 Assistive Technologies and Prosthetics           201
• 5.5.7 Market Size and Forecast, in Healthcare and Medical Devices (2024-2037), Millions USD          203
• 5.6.1 Applications      204
• 5.6.2 Technology Roadmap 205
• 5.6.3 Unmanned Aerial Vehicles (UAVs) and Drones           206
• 5.6.4 Satellite Imaging and Remote Sensing             208
• 5.6.5 Missile Guidance and Target Recognition      209
• 5.6.6 Cybersecurity and Threat Detection: 211
• 5.6.7 Market Size and Forecast, in Aerospace and Defence (2024-2037), Millions USD              212
• 5.7.1 Applications      213
• 5.7.2 Technology Roadmap 214
• 5.7.3 High-performance Computing and Scientific Simulations:                215
• 5.7.4 Big Data Analytics and Machine Learning      216
• 5.7.5 Cloud-based AI Services and Platforms          218
• 5.7.6 Energy-efficient Datacenter Infrastructure    219
• 5.7.7 Market Size and Forecast, in Datacenters and Cloud Services (2024-2037), Millions USD           220
• 5.8.1 North America  223
• 5.8.2 Europe   224
• 5.8.3 Asia-Pacific       225
• 5.8.4 Rest of the World            227
• 5.9.1 Overview of the Neuromorphic Computing and Sensing Ecosystem           228
• 5.9.2 Neuromorphic Chip Manufacturers and Processors              229
• 5.9.3 Neuromorphic Sensor Manufacturers              230
• 5.9.4 Emerging Non-Volatile Memory (eNVM) Manufacturers       231
• 5.9.5 Neuromorphic Software and Framework Providers 232
• 5.9.6 Research Institutions and Academia 232
• 5.10.1 Quantum Computing               236
• 5.10.2 Photonic Computing 236
• 5.10.3 DNA Computing           237
• 5.10.4 Spintronic Computing              237
• 5.10.5 Chemical Computing               237
• 5.10.6 Superconducting Computing              237
• 5.10.7 Analog AI Chips            237
• 5.10.8 In-Memory Computing            238
• 5.10.9 Reversible Computing             238
• 5.10.10 Quantum Dot Computing   238
• 5.10.11 Technology Substitution Analysis  239
• 5.10.12 Migration Pathways 241
• 5.10.13 Comparative Advantages/Disadvantages 241
• 5.3 Automotive and Transportation 176
• 5.4 Industrial and Manufacturing     187
• 5.5 Healthcare and Medical Devices              195
• 5.6 Aerospace and Defense 204
• 5.7 Datacenters and Cloud Services              213
• 5.8 Regional Market Analysis and Forecasts             222
• 5.9 Competitive Landscape and Key Players            228
• 5.10 Competing Emerging Technologies      235

6. Company Profiles 243 (154 company profiles)

7. Convergence with the Broader Neural Networks Market 711

• 7.1 Neuromorphic Computing in the Neural Networks Ecosystem            711
• 7.2 Growth Trajectory of Novel Neural Network Hardware               711
• 7.3 Spiking Neural Networks and Architecture Convergence         711
• 7.4 Key Neuromorphic Companies Within the Neural Networks Landscape       711
• 7.5 Energy Efficiency as a Structural Driver                712

8. Research Scope & Methodology 713

9. References 714