한글목차

뉴로모픽 컴퓨팅 및 센싱 시장은 에너지 효율적인 AI 처리와 실시간 센서 용도에 대한 수요 증가로 인해 빠르게 성장하고 있습니다. 현재 시장은 프로세서와 메모리 시스템을 포함한 뉴로모픽 컴퓨팅 하드웨어와 이벤트 기반 비전 센서를 중심으로 한 뉴로모픽 센싱 디바이스로 구분되며, 인텔, IBM, 삼성 등 주요 반도체 기업들이 뉴로모픽 프로세서를 상용화하고 있습니다. 뉴로모픽 프로세서를 상용화하고 있으며, Prophesee, iniVation과 같은 전문 기업들이 뉴로모픽 센싱 기술을 주도하고 있습니다. 생태계는 특정 용도나 기술적 접근 방식에 집중하는 신규 시장 진출기업들에 의해 계속 확장되고 있습니다.

주요 시장 성장 촉진요인은 엣지에서의 에너지 효율적인 AI 처리에 대한 요구 증가, 자율 시스템에서 실시간 처리에 대한 요구 증가, 이러한 요구를 충족시키기 위한 기존 컴퓨팅 아키텍처의 한계 등으로 요약할 수 있습니다. 자동차 부문은 특히 첨단운전자보조시스템(ADAS)와 자율주행차의 뉴로모픽 비전 센서에 있어 큰 시장 기회가 되고 있습니다. 로봇 공학 및 품질 관리와 같은 산업 분야도 하나의 큰 시장 부문을 형성하고 있습니다.

뉴로모픽 비전 센서는 여러 주요 산업 분야에서 채택이 가속화되고 있습니다. 의료 분야, 특히 의료 영상 및 신경 인터페이스 분야에서 뉴로모픽 비전 센서의 활용도가 높아지고 있습니다. 항공우주 및 방위 산업은 자율 시스템 및 신호 처리에 사용되는 뉴로모픽 기술에 대한 투자가 증가하고 있습니다. 소비자 전자제품 제조업체들은 차세대 모바일 기기 및 IoT 용도를 위한 뉴로모픽 솔루션을 모색하고 있으며, 2035년까지 기술의 성숙과 새로운 용도의 등장으로 시장이 크게 변화할 것으로 예상됩니다. 재료 과학과 제조 공정의 기술 발전으로 비용 절감과 성능 향상이 예상되며, 더 폭넓은 채택이 가능해질 것입니다. 표준화된 개발 도구와 소프트웨어 생태계의 발전으로 기존 시스템에 쉽게 도입하고 통합할 수 있게 될 것입니다.

시장의 성장은 표준화의 필요성, 현재의 높은 제조 비용, 대체 기술과의 경쟁 등 여러 가지 문제에 직면해 있습니다. 그러나 에너지 효율과 실시간 처리에서 뉴로모픽 접근법의 근본적인 장점은 투자 및 개발을 촉진하는 요인으로 작용하고 있습니다. 뉴로모픽 요소와 기존 컴퓨팅 아키텍처를 결합한 하이브리드 시스템의 등장은 시장 채택을 가속화할 수 있는 중요한 트렌드를 보여주고 있습니다.

이 부문에 대한 투자는 계속 확대되고 있으며, 벤처 캐피탈과 기업 자금 지원은 기술 개발 및 상용화를 지원하고 있습니다. 기술 공급자, 제조업체, 최종 사용자 간의 전략적 파트너십은 개발에서 점점 더 중요해지고 있습니다. 생태계는 전문 소프트웨어 제공업체, 시스템 통합사업자, 구현 및 배포를 지원하는 서비스 업체 등으로 진화하고 있습니다. 향후 시장 성장은 AI, 엣지 컴퓨팅, IoT와 같은 보완 기술의 발전에 영향을 받을 가능성이 높습니다. 뉴로모픽 기술과 이러한 영역과의 통합은 새로운 응용 기회와 시장 부문을 창출할 수 있습니다. 시장에서 성공하기 위해서는 기술 혁신과 실용적인 구현 고려사항의 균형을 맞추고, 특정 용도에 대한 가치를 명확하게 제시해야 합니다.

세계의 뉴로모픽 컴퓨팅 및 센싱(Neuromorphic Computing and Sensing) 시장에 대해 조사 분석했으며, 2035년까지 시장 동향, 기술 개발, 성장 기회에 대해 상세하게 조사 분석하여 전해드립니다.

목차

제1장 주요 요약

• 뉴로모픽 컴퓨팅 및 센싱 시장 개요
• 무어의 법칙 종식
• 시장 실적
• 2024년 시장
• 주요 시장 동향과 성장 촉진요인
• 시장이 해결해야 할 과제와 한계
• 향후 전망과 기회
• 뉴로모픽 컴퓨팅과 생성형 AI
• 시장 밸류체인
• 시장 맵
• 자금조달과 투자
• 전략적 파트너십과 제휴
• 규제와 윤리에 관한 우려
• 지속가능성과 환경에 대한 영향

제2장 서론

• 뉴로모픽 컴퓨팅 및 센싱 정의와 개념
• 주요 뉴로모픽 접근
• 뉴로모픽 시스템 제조 공정
• 주요 재료 공급업체
• 공급망 취약성과 완화책
• 제조능력 분석
• 품질관리와 검사 절차
• 기존 컴퓨팅과 센싱 접근 비교
• 뉴로모픽 컴퓨팅과 양자 컴퓨팅
• 주요 특징과 이점
• 시장과 용도

제3장 뉴로모픽 컴퓨팅 기술과 아키텍처

• Spiking Neural Networks (SNNs)
• 뉴로모픽 컴퓨팅용 메모리 아키텍처
• 뉴로모픽 하드웨어 및 프로세서
• 뉴로모픽 컴퓨팅용 소프트웨어 및 프레임워크

제4장 뉴로모픽 센싱 기술 및 아키텍처

• 이벤트 기반 센서와 처리
• 하이브리드 센싱 접근
• 뉴로모픽 센서 아키텍처와 설계
• 신호 처리와 특징 추출 기법

제5장 시장 분석과 예측

• 모바일 및 소비자 용도
• 자동차 및 운송
• 산업 및 제조
• 의료 및 의료기기
• 항공우주 및 방위
• 데이터센터 및 ㄴ클라우드 서비스
• 지역 시장 분석과 예측
• 경쟁 구도와 주요 기업
• 경쟁 하는 신기술

제6장 기업 개요(기업 143개사 개요)

제7장 조사 범위와 조사 방법

제8장 참고 문헌

영문목차

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

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

• 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. 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
    • 3.2.2.1. 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. 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. 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

6. COMPANY PROFILES (143 company profiles)

7. RESEARCH SCOPE & METHODOLOGY

8. REFERENCES