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神经形态计算/感测的全球市场（2024-2035）
市场调查报告书
商品编码
1486555

神经形态计算/感测的全球市场（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 | 订单完成后即时交付

价格

神经形态计算/感测致力于开发受人脑结构和功能启发的技术。该市场目的是创建更有效率、适应性更强的智慧系统，能够以类似于生物神经网路的方式处理资讯。神经形态技术有潜力彻底改变各种产业和应用，包括人工智慧、边缘运算、自主系统和感测。对节能即时处理解决方案不断成长的需求推动了市场的发展，特别是在边缘设备和物联网应用中。与传统运算方法相比，神经形态系统具有多种优势，包括低延迟、高可扩展性以及学习和适应新情况的能力。

神经形态计算/感测的主要应用领域如下。

  • 边缘人工智慧、物联网：神经形态技术可以更有效率、更智慧地处理边缘资料，减少对云端连接的需求并提高隐私和安全性。
  • 自动驾驶汽车、机器人：神经形态系统可在自动驾驶汽车和机器人中实现即时决策、物件辨识和导航。
  • 网路安全、异常检测：神经形态系统学习和适应新模式的能力使其适合检测网路系统中的异常和潜在威胁。
  • 智慧感测器、监控系统：神经形态感测器可在工业监控、环境感测和智慧城市基础设施等各种应用中有效地取得和处理资料。
  • 高效能运算、科学模拟：神经形态技术可以加速气候建模、药物发现和材料科学等领域的复杂运算和模拟。

由于技术进步、各行业采用率的不断提高以及对更高效、更智慧的计算解决方案的需求，神经形态计算/感测市场预计在未来几年将显着成长。

本报告研究了全球神经形态计算/感测市场，并对主要趋势、成长驱动因素、挑战和未来机会进行了深入分析。

目录

第1章 执行摘要

  • 神经拟态计算/感测市场概览
    • 全球市场收入（2024-2035年）
    • 市场区隔
  • 摩尔定律的终结
  • 市场表现
  • 市场（2024年）
  • 主要市场趋势与成长因素
  • 市场挑战与限制
  • 未来的前景与机会
    • 新趋势
    • 技术路线图
  • 神经拟态运算与生成式人工智慧
  • 市场价值链
  • 市场地图
  • 融资与投资
  • 策略伙伴关係与联盟
  • 监管和道德考虑
    • 资料隐私和安全
    • 神经形态系统中的偏见与公平性
    • 智慧财产权和专利状况
    • 环境影响与永续性

第2章 简介

  • 神经形态计算/感测的定义与概念
  • 主要的神经形态方法
    • 大规模硬体神经形态计算系统
    • 非挥发性储存技术
    • 先进忆阻材料与装置
  • 与传统计算/感测方法的比较
  • 神经拟态计算与量子计算
  • 主要特点和优点
    • 低延迟、即时处理
    • 电源效率、节能
    • 可扩展性、适应性
    • 线上学习，自主决策
  • 市场与应用
    • 边缘人工智慧、物联网
    • 自动驾驶汽车、机器人
    • 网路安全、异常侦测
    • 智慧感测器、监控系统
    • 资料中心、高效能运算

第3章 神经形态运算技术与架构

  • 脉衝神经网路（SNN）
    • 生物学灵感与原理
    • SNN 类型及其特征
    • SNN 的优点和局限性
  • 神经形态运算的记忆体架构
    • 传统记忆体方法（SRAM、DRAM）
    • 新型非挥发性记忆体（eNVM）技术
    • 记忆体计算、近记忆体计算
    • 混合记忆体架构
  • 神经形态硬体、处理器
    • 数位神经形态处理器
    • 类比神经形态处理器
    • 混合讯号神经形态处理器
    • 基于 FPGA 的神经拟态系统
    • 神经形态加速器、协处理器
  • 用于神经形态计算的软体和框架
    • 神经拟态程式语言与工具
    • 神经拟态模拟平台、框架
    • 神经拟态演算法库、储存库
    • 神经形态软体开发套件（SDK）

第4章 神经形态感测技术与架构

  • 基于事件的传感器和处理
    • 神经形态视觉感测器
    • 神经形态听觉感测器
    • 神经形态嗅觉感应器
    • 事件驱动的处理与演算法
  • 混合感测方法
    • 传统感测器和基于事件的感测器的结合
    • 多种感测方法的融合
    • 混合感测的优势与挑战
  • 神经形态感测器架构与设计
    • 像素级处理与计算
    • 感测器和处理器的协同设计和集成
    • 仿生感测器设计与材料
  • 讯号处理、特征撷取技术
    • 基于尖峰的编码和解码
    • 时间和时空特征抽取
    • 神经拟态过滤、杂讯消除
    • 自适应、学习处理

第5章 市场分析与预测

  • 行动设备，消费者使用
    • 智慧型手机、穿戴式装置
    • 智慧家庭、物联网设备
    • 消费者健康
    • 娱乐、游戏
  • 汽车、交通
    • 先进驾驶辅助系统（ADAS）
    • 自动驾驶汽车、机器人计程车
    • 汽车资讯娱乐、使用者体验
    • 智慧交通管理、基础设施
  • 工业、製造业
    • 工业物联网、智慧工厂
    • 预测性维护、异常检测
    • 品质控制、检验
    • 物流、供应链优化
  • 医疗、医疗设备
    • 医学影像、诊断
    • 穿戴式健康监测设备
    • 个人化医疗、药物发现
    • 辅助科技、义肢
  • 航空航太、国防
    • 无人机（UAV）、无人机
    • 卫星影像、遥感
    • 飞弹导引、目标识别
    • 网路安全、威胁侦测
  • 资料中心、云端服务
    • 高效能运算、科学模拟
    • 巨量资料分析、机器学习
    • 基于云端的人工智慧服务和平台
    • 节能资料中心基础设施
  • 区域市场分析与预测
    • 北美
    • 欧洲
    • 亚太地区
    • 其他领域
  • 竞争状况和主要公司
    • 神经形态计算/感测生态系概述
    • 神经拟态晶片製造商、处理器
    • 神经形态感测器製造商
    • 新的非挥发性记忆体（eNVM）製造商
    • 神经拟态软体和框架供应商
    • 研究机构、学术机构

第6章 公司简介（141家公司简介）

第7章 研究范围、研究方法

第8章 参考文献

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