查看购物车
  • Traditional Chinese
  • English
  • Japanese
神经形态运算·感测的全球市场(2025年～2035年)
市场调查报告书
商品编码
1655855

神经形态运算·感测的全球市场(2025年～2035年)

The Global Neuromorphic Computing and Sensing Market 2025-2035
出版日期: | 出版商: Future Markets, Inc. | 英文 284 Pages, 79 Tables, 62 Figures | 订单完成后即时交付

价格

由于对节能人工智慧处理和即时感测器应用的需求不断增加，神经形态运算和感测市场正在经历快速成长。市场目前细分为神经形态运算硬件，包括处理器和储存系统，以及神经形态感测设备，主要是基于事件的视觉感测器。英特尔、IBM 和三星等主要半导体公司正在将神经形态处理器商业化，而 Prophesee 和 iniVation 等专业公司则在神经形态感测技术方面处于领先地位。随着新进入者专注于特定用例或技术方法，生态系统不断扩大。

关键的市场推动因素包括对边缘节能 AI 处理的日益增长的需求、对自主系统即时处理的日益增长的需求，以及传统运算架构在满足这些需求方面的局限性。汽车领域为神经形态视觉感测器提供了巨大的市场机会，尤其是在 ADAS（高级驾驶辅助系统）和自动驾驶汽车领域。机器人和品质控制等工业应用也构成了很大的细分市场。

神经形态视觉感测器正在多个主要产业中加速应用。医疗应用正在兴起，尤其是在医学影像和神经介面领域。航空航太和国防领域正在大力投资用于自主系统和讯号处理的神经形态技术。消费性电子产品製造商正在探索用于下一代行动装置和物联网应用的神经形态解决方案。随着我们迈向 2035 年，随着技术的成熟和新应用的出现，市场预计将发生重大变化。材料科学和製造流程的技术进步可望降低成本、提高性能，从而实现更广泛的应用。标准化开发工具和软体生态系统的发展将使其更容易引入和整合到现有系统中。

市场成长面临多重挑战，包括标准化的需求、当前高昂的製造成本以及来自替代技术的竞争。然而，神经形态方法在能源效率和即时处理方面的根本优势继续推动投资和发展。将神经形态元素与传统运算架构结合的混合系统的出现代表着可能加速市场采用的重要趋势。

该领域的投资持续成长，风险资本和企业资金都支持技术开发和商业化。技术提供者、製造商和最终用户之间的策略伙伴关係在发展中变得越来越重要。生态系统正在不断发展，包括专业软体供应商、系统整合商和支援实施和部署的服务公司。未来的市场成长可能会受到人工智慧、边缘运算和物联网等互补技术进步的影响。神经形态技术与这些学科的结合可能会创造新的应用机会和细分市场。要在市场上取得成功，就需要平衡技术创新与实际实施考虑，并清楚地展示特定应用的价值。

本报告研究了全球神经形态计算和感测市场，并详细介绍了 2035 年之前的市场趋势、技术发展和成长机会。

目录

第1章 摘要整理

  • 神经形态计算与感测市场概览
  • 摩尔定律的终结
  • 市场表现
  • 2024 年的市场
  • 主要市场趋势与成长动力
  • 市场挑战与限制
  • 未来展望与机遇
  • 神经形态运算与生成式人工智慧
  • 市场价值链
  • 市场地图
  • 资金和投资
  • 策略伙伴关係与联盟
  • 监管与道德问题
  • 永续性和环境影响

第2章 简介

  • 神经形态运算·感测定义和概念
  • 主要的神经形态方法
  • 神经形态系统的製造流程
  • 主要材料供应商
  • 与供应链的漏洞缓和策略
  • 製造能力分析
  • 品管与检查次序
  • 传统的运算和感测方法的比较
  • 神经形态运算和量子运算
  • 主要的特征与优点
  • 市场与用途

第3章 神经形态运算技术和架构

  • 脉衝神经网路(SNN)
  • 神经形态运算记忆体架构
  • 神经形态硬体设备，处理器
  • 神经形态运算软体，组成架构

第4章 神经形态感测技术，架构

  • 活动为基础的感测器与处理
  • 混合感测方法
  • 神经形态感测器的架构与设计
  • 信号处理和特征抽取技术

第5章 市场分析与预测

  • 行动，消费者用途
  • 汽车，运输
  • 工业，製造
  • 医疗，医疗设备
  • 航太，防卫
  • 资料中心，云端服务
  • 地区市场分析与预测
  • 竞争情形和主要企业
  • 竞争的新技术

第6章 企业简介(企业143公司的简介)

第7章 调查范围和调查手法

第8章 参考文献

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

List of Tables

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

List of Figures

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