뉴로모픽 컴퓨팅 및 센싱 시장(2027-2037년)

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

Table of Contents

1. Executive Summary

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

2. Introduction

• 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. Neuromorphic Computing Technologies and Architecture

• 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
  • 3.2.3 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 Software and Programming Ecosystem Developments
  • 3.4.4 Neuromorphic Algorithm Libraries and Repositories
  • 3.4.5 Neuromorphic Software Development Kits (SDKs)

4. Neuromorphic Sensing Technologies and Architectures

• 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. Market Analysis and Forecasts

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

6. Company Profiles (154 company profiles)

7. Convergence with the Broader Neural Networks Market

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

8. Research Scope & Methodology

9. References