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神經形態運算·感測的全球市場(2025年～2035年)
市場調查報告書
商品編碼
1655855

神經形態運算·感測的全球市場(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 | 訂單完成後即時交付

價格

由於對節能人工智慧處理和即時感測器應用的需求不斷增加，神經形態運算和感測市場正在經歷快速成長。市場目前細分為神經形態運算硬件，包括處理器和儲存系統，以及神經形態感測設備，主要是基於事件的視覺感測器。英特爾、IBM 和三星等主要半導體公司正在將神經形態處理器商業化，而 Prophesee 和 iniVation 等專業公司則在神經形態感測技術方面處於領先地位。隨著新進入者專注於特定用例或技術方法，生態系統不斷擴大。

關鍵的市場推動因素包括對邊緣節能 AI 處理的日益增長的需求、對自主系統即時處理的日益增長的需求，以及傳統運算架構在滿足這些需求方面的局限性。汽車領域為神經形態視覺感測器提供了巨大的市場機會，尤其是在 ADAS（高級駕駛輔助系統）和自動駕駛汽車領域。機器人和品質控制等工業應用也構成了很大的細分市場。

神經形態視覺感測器正在多個主要產業中加速應用。醫療應用正在興起，尤其是在醫學影像和神經介面領域。航空航太和國防領域正在大力投資用於自主系統和訊號處理的神經形態技術。消費性電子產品製造商正在探索用於下一代行動裝置和物聯網應用的神經形態解決方案。隨著我們邁向 2035 年，隨著技術的成熟和新應用的出現，市場預計將發生重大變化。材料科學和製造流程的技術進步可望降低成本、提高性能，從而實現更廣泛的應用。標準化開發工具和軟體生態系統的發展將使其更容易引入和整合到現有系統中。

市場成長面臨多重挑戰，包括標準化的需求、當前高昂的製造成本以及來自替代技術的競爭。然而，神經形態方法在能源效率和即時處理方面的根本優勢繼續推動投資和發展。將神經形態元素與傳統運算架構結合的混合系統的出現代表著可能加速市場採用的重要趨勢。

該領域的投資持續成長，風險資本和企業資金都支持技術開發和商業化。技術提供者、製造商和最終用戶之間的策略夥伴關係在發展中變得越來越重要。生態系統正在不斷發展，包括專業軟體供應商、系統整合商和支援實施和部署的服務公司。未來的市場成長可能會受到人工智慧、邊緣運算和物聯網等互補技術進步的影響。神經形態技術與這些學科的結合可能會創造新的應用機會和細分市場。要在市場上取得成功，就需要平衡技術創新與實際實施考慮，並清楚地展示特定應用的價值。

本報告研究了全球神經形態計算和感測市場，並詳細介紹了 2035 年之前的市場趨勢、技術發展和成長機會。

目錄

第1章 摘要整理

  • 神經形態計算與感測市場概覽
  • 摩爾定律的終結
  • 市場表現
  • 2024 年的市場
  • 主要市場趨勢與成長動力
  • 市場挑戰與限制
  • 未來展望與機遇
  • 神經形態運算與生成式人工智慧
  • 市場價值鏈
  • 市場地圖
  • 資金和投資
  • 策略夥伴關係與聯盟
  • 監管與道德問題
  • 永續性和環境影響

第2章 簡介

  • 神經形態運算·感測定義和概念
  • 主要的神經形態方法
  • 神經形態系統的製造流程
  • 主要材料供應商
  • 與供應鏈的漏洞緩和策略
  • 製造能力分析
  • 品管與檢查次序
  • 傳統的運算和感測方法的比較
  • 神經形態運算和量子運算
  • 主要的特徵與優點
  • 市場與用途

第3章 神經形態運算技術和架構

  • 脈衝神經網路(SNN)
  • 神經形態運算記憶體架構
  • 神經形態硬體設備，處理器
  • 神經形態運算軟體，組成架構

第4章 神經形態感測技術，架構

  • 活動為基礎的感測器與處理
  • 混合感測方法
  • 神經形態感測器的架構與設計
  • 信號處理和特徵抽取技術

第5章 市場分析與預測

  • 行動，消費者用途
  • 汽車，運輸
  • 工業，製造
  • 醫療，醫療設備
  • 航太，防衛
  • 資料中心，雲端服務
  • 地區市場分析與預測
  • 競爭情形和主要企業
  • 競爭的新技術

第6章 企業簡介(企業143公司的簡介)

第7章 調查範圍和調查手法

第8章 參考文獻

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