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神經形態計算/感測的全球市場（2024-2035）
市場調查報告書
商品編碼
1486555

神經形態計算/感測的全球市場（2024-2035）

The Global Market for Neuromorphic Computing and Sensing 2024-2035
出版日期: | 出版商: Future Markets, Inc. | 英文 316 Pages, 74 Tables, 62 Figures | 訂單完成後即時交付

價格

神經形態計算/感測致力於開發受人腦結構和功能啟發的技術。該市場目的是創建更有效率、適應性更強的智慧系統，能夠以類似於生物神經網路的方式處理資訊。神經形態技術有潛力徹底改變各種產業和應用，包括人工智慧、邊緣運算、自主系統和感測。對節能即時處理解決方案不斷成長的需求推動了市場的發展，特別是在邊緣設備和物聯網應用中。與傳統運算方法相比，神經形態系統具有多種優勢，包括低延遲、高可擴展性以及學習和適應新情況的能力。

神經形態計算/感測的主要應用領域如下。

  • 邊緣人工智慧、物聯網：神經形態技術可以更有效率、更智慧地處理邊緣資料，減少對雲端連接的需求並提高隱私和安全性。
  • 自動駕駛汽車、機器人：神經形態系統可在自動駕駛汽車和機器人中實現即時決策、物件辨識和導航。
  • 網路安全、異常檢測：神經形態系統學習和適應新模式的能力使其適合檢測網路系統中的異常和潛在威脅。
  • 智慧感測器、監控系統：神經形態感測器可在工業監控、環境感測和智慧城市基礎設施等各種應用中有效地取得和處理資料。
  • 高效能運算、科學模擬：神經形態技術可以加速氣候建模、藥物發現和材料科學等領域的複雜運算和模擬。

由於技術進步、各行業採用率的不斷提高以及對更高效、更智慧的計算解決方案的需求，神經形態計算/感測市場預計在未來幾年將顯著成長。

本報告研究了全球神經形態計算/感測市場，並對主要趨勢、成長驅動因素、挑戰和未來機會進行了深入分析。

目錄

第1章 執行摘要

  • 神經擬態計算/感測市場概覽
    • 全球市場收入（2024-2035年）
    • 市場區隔
  • 摩爾定律的終結
  • 市場表現
  • 市場（2024年）
  • 主要市場趨勢與成長因素
  • 市場挑戰與限制
  • 未來的前景與機會
    • 新趨勢
    • 技術路線圖
  • 神經擬態運算與生成式人工智慧
  • 市場價值鏈
  • 市場地圖
  • 融資與投資
  • 策略夥伴關係與聯盟
  • 監管和道德考慮
    • 資料隱私和安全
    • 神經形態系統中的偏見與公平性
    • 智慧財產權和專利狀況
    • 環境影響與永續性

第2章 簡介

  • 神經形態計算/感測的定義與概念
  • 主要的神經形態方法
    • 大規模硬體神經形態計算系統
    • 非揮發性儲存技術
    • 先進憶阻材料與裝置
  • 與傳統計算/感測方法的比較
  • 神經擬態計算與量子計算
  • 主要特點和優點
    • 低延遲、即時處理
    • 電源效率、節能
    • 可擴展性、適應性
    • 線上學習，自主決策
  • 市場與應用
    • 邊緣人工智慧、物聯網
    • 自動駕駛汽車、機器人
    • 網路安全、異常偵測
    • 智慧感測器、監控系統
    • 資料中心、高效能運算

第3章 神經形態運算技術與架構

  • 脈衝神經網路（SNN）
    • 生物學靈感與原理
    • SNN 類型及其特徵
    • SNN 的優點和局限性
  • 神經形態運算的記憶體架構
    • 傳統記憶體方法（SRAM、DRAM）
    • 新型非揮發性記憶體（eNVM）技術
    • 記憶體計算、近記憶體計算
    • 混合記憶體架構
  • 神經形態硬體、處理器
    • 數位神經形態處理器
    • 類比神經形態處理器
    • 混合訊號神經形態處理器
    • 基於 FPGA 的神經擬態系統
    • 神經形態加速器、協處理器
  • 用於神經形態計算的軟體和框架
    • 神經擬態程式語言與工具
    • 神經擬態模擬平台、框架
    • 神經擬態演算法庫、儲存庫
    • 神經形態軟體開發套件（SDK）

第4章 神經形態感測技術與架構

  • 基於事件的傳感器和處理
    • 神經形態視覺感測器
    • 神經形態聽覺感測器
    • 神經形態嗅覺感應器
    • 事件驅動的處理與演算法
  • 混合感測方法
    • 傳統感測器和基於事件的感測器的結合
    • 多種感測方法的融合
    • 混合感測的優勢與挑戰
  • 神經形態感測器架構與設計
    • 像素級處理與計算
    • 感測器和處理器的協同設計和集成
    • 仿生感測器設計與材料
  • 訊號處理、特徵擷取技術
    • 基於尖峰的編碼和解碼
    • 時間和時空特徵抽取
    • 神經擬態過濾、雜訊消除
    • 自適應、學習處理

第5章 市場分析與預測

  • 行動設備，消費者使用
    • 智慧型手機、穿戴式裝置
    • 智慧家庭、物聯網設備
    • 消費者健康
    • 娛樂、遊戲
  • 汽車、交通
    • 先進駕駛輔助系統（ADAS）
    • 自動駕駛汽車、機器人計程車
    • 汽車資訊娛樂、使用者體驗
    • 智慧交通管理、基礎設施
  • 工業、製造業
    • 工業物聯網、智慧工廠
    • 預測性維護、異常檢測
    • 品質控制、檢驗
    • 物流、供應鏈優化
  • 醫療、醫療設備
    • 醫學影像、診斷
    • 穿戴式健康監測設備
    • 個人化醫療、藥物發現
    • 輔助科技、義肢
  • 航空航太、國防
    • 無人機（UAV）、無人機
    • 衛星影像、遙感
    • 飛彈導引、目標識別
    • 網路安全、威脅偵測
  • 資料中心、雲端服務
    • 高效能運算、科學模擬
    • 巨量資料分析、機器學習
    • 基於雲端的人工智慧服務和平台
    • 節能資料中心基礎設施
  • 區域市場分析與預測
    • 北美
    • 歐洲
    • 亞太地區
    • 其他領域
  • 競爭狀況和主要公司
    • 神經形態計算/感測生態系概述
    • 神經擬態晶片製造商、處理器
    • 神經形態感測器製造商
    • 新的非揮發性記憶體（eNVM）製造商
    • 神經擬態軟體和框架供應商
    • 研究機構、學術機構

第6章 公司簡介（141家公司簡介）

第7章 研究範圍、研究方法

第8章 參考文獻

Neuromorphic computing and sensing focuses on developing technologies inspired by the structure and function of the human brain. This market aims to create more efficient, adaptive, and intelligent systems that can process information in ways similar to biological neural networks. Neuromorphic technologies have the potential to revolutionize various industries and applications, including artificial intelligence (AI), edge computing, autonomous systems, and sensing. The neuromorphic computing and sensing market is driven by the increasing demand for energy-efficient and real-time processing solutions, particularly in edge devices and IoT applications. Neuromorphic systems offer several advantages over traditional computing approaches, such as low latency, high scalability, and the ability to learn and adapt to new situations.

Key application areas for neuromorphic computing and sensing include:

  • Edge AI and IoT: Neuromorphic technologies enable more efficient and intelligent processing of data at the edge, reducing the need for cloud connectivity and improving privacy and security.
  • Autonomous vehicles and robotics: Neuromorphic systems can enable real-time decision-making, object recognition, and navigation in autonomous vehicles and robots.
  • Cybersecurity and anomaly detection: The ability of neuromorphic systems to learn and adapt to new patterns makes them well-suited for detecting anomalies and potential threats in cyber systems.
  • Smart sensors and monitoring systems: Neuromorphic sensors can efficiently capture and process data in various applications, such as industrial monitoring, environmental sensing, and smart city infrastructure.
  • High-performance computing and scientific simulations: Neuromorphic technologies can accelerate complex computations and simulations in fields like climate modeling, drug discovery, and materials science.

The neuromorphic computing and sensing market is expected to grow significantly in the coming years, driven by advancements in technology, increasing adoption across various industries, and the need for more efficient and intelligent computing solutions.

"The Global Market for Neuromorphic Computing and Sensing 2024-2035" offers in-depth insights into the rapidly evolving field of neuromorphic technologies and their transformative impact on artificial intelligence, edge computing, and autonomous systems. This extensive study provides a detailed analysis of the neuromorphic computing and sensing market, including key trends, growth drivers, challenges, and future opportunities. The report explores the diverse applications and industries adopting neuromorphic technologies, such as edge AI and IoT, autonomous vehicles and robotics, cybersecurity, smart sensors, and high-performance computing. It also examines the competitive landscape, highlighting major players in neuromorphic chip manufacturing, sensor development, emerging non-volatile memory solutions, and software and framework provision.

One of the key features of this report is the market size and forecast data segmented by application, market, and region, providing a holistic view of the market's growth potential. The report delves into the technical aspects of neuromorphic computing and sensing, covering spiking neural networks, event-based sensors, memory architectures, hardware and processor designs, and software and frameworks. Additionally, the report identifies critical challenges and opportunities facing the neuromorphic industry, including scalability, standardization, algorithm development, power management, data privacy, and security. It also explores emerging trends such as hybrid neuromorphic-conventional systems, edge AI, quantum computing, explainable AI, and brain-computer interfaces.

The report also includes a comprehensive analysis of the market value chain, funding and investments, strategic partnerships, and collaborations within the neuromorphic industry. It also addresses the regulatory and ethical considerations surrounding neuromorphic technologies.

With over 140 company profiles, "The Global Market for Neuromorphic Computing and Sensing 2024-2035" offers valuable insights for technology providers, manufacturers, end-users, investors, and policymakers looking to understand and capitalize on the immense potential of neuromorphic technologies in shaping the future of AI, edge computing, and autonomous systems. The neuromorphic computing and sensing market is characterized by a growing ecosystem of technology providers, manufacturers, research institutions, and end-users. Major players in this market include established semiconductor companies, startups, and academic and research organizations. Companies profiled include Aspinity, BrainChip, Celestial AI, DeepCube, Deeplite, Expedera, Huawei, IBM, iniVation, Innatera, Instar-Robotics, Intrinsic, Microsoft, Oculi, Perceive, POLYN, Prophesee, Qualcomm, Rain AI, SpiNNcloud Systems, SynSense, Syntiant, and Voxel Sensors.

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. New Application Areas and Use Cases
      • 1.7.1.7. Disruptive Business Models and Services
      • 1.7.1.8. Collaborative Ecosystem Development
      • 1.7.1.9. 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
    • 1.13.4. Environmental Impact and Sustainability

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. Comparison with traditional computing and sensing approaches
  • 2.4. Neuromorphic computing vs. quantum computing
  • 2.5. Key features and advantages
    • 2.5.1. Low latency and real-time processing
    • 2.5.2. Power efficiency and energy savings
    • 2.5.3. Scalability and adaptability
    • 2.5.4. Online learning and autonomous decision-making
  • 2.6. Markets and Applications
    • 2.6.1. Edge AI and IoT
    • 2.6.2. Autonomous Vehicles and Robotics
    • 2.6.3. Cybersecurity and Anomaly Detection
    • 2.6.4. Smart Sensors and Monitoring Systems
    • 2.6.5. 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

6. COMPANY PROFILES(141 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. Comparison with traditional computing and sensing approaches
  • Table 15. Comparison between neuromorphic and quantum computing
  • Table 16. Key features and advantages of neuromorphic computing and sensing
  • Table 17. Markets and Applications of neuromorphic computing and sensing
  • Table 18. Von neumann architecture versus neuromorphic architecture
  • Table 19. Types of SNNs and their characteristics
  • Table 20. Advantages and limitations of SNNs
  • Table 21. Conventional memory approaches (SRAM, DRAM)
  • Table 22. Emerging non-volatile memory (eNVM) technologies
  • Table 23. Hybrid memory architectures
  • Table 24. Neuromorphic accelerators and co-processors
  • Table 25. Neuromorphic programming languages and tools
  • Table 26. Neuromorphic simulation platforms and frameworks
  • Table 27. Neuromorphic algorithm libraries and repositories
  • Table 28. Neuromorphic software development kits (SDKs)
  • Table 29. Hybrid sensing approaches
  • Table 30. Advantages and challenges of hybrid sensing
  • Table 31. Bio-inspired sensor designs and materials
  • Table 32. Signal Processing and Feature Extraction Techniques
  • Table 33. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application
  • Table 34. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application
  • Table 35. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application
  • Table 36. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application
  • Table 37. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD
  • Table 38. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application
  • Table 39. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application
  • Table 40. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application
  • Table 41. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application
  • Table 42. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD
  • Table 43. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application
  • Table 44. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application
  • Table 45. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application
  • Table 46. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application
  • Table 47. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD
  • Table 48. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application
  • Table 49. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application
  • Table 50. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application
  • Table 51. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application
  • Table 52. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD
  • Table 53. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application
  • Table 54. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application
  • Table 55. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application
  • Table 56. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application
  • Table 57. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD
  • Table 58. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application
  • Table 59. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application
  • Table 60. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application
  • Table 61. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application
  • Table 62. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD
  • Table 63. Market revenues for neuromorphic computing and sensing by region from 2024-2035 in millions USD
  • Table 64. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD
  • Table 65. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD
  • Table 66. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2035), millions USD
  • Table 67. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2035), millions USD
  • Table 68. Neuromorphic Chip Manufacturers and Their Product Offerings
  • Table 69. Neuromorphic Sensor Manufacturers and Their Product Offerings
  • Table 70. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings
  • Table 71. Neuromorphic Software and Framework Providers and Their Solutions
  • Table 72. Key Research Institutions and Academia in Neuromorphic Computing and Sensing
  • Table 73. Evolution of Apple Neural Engine
  • Table 74. 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