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The Global Market for Neuromorphic Computing and Sensing 2024-2035

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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.

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1             EXECUTIVE SUMMARY            17

  • 1.1        Overview of the neuromorphic computing and sensing market   17
    • 1.1.1    Global Market Revenues 2024-2035              19
    • 1.1.2    Market segmentation               20
  • 1.2        Ending of Moore’s Law             22
  • 1.3        Historical market         23
  • 1.4        The market in 2024    24
  • 1.5        Key market trends and growth drivers            25
  • 1.6        Market challenges and limitations   26
  • 1.7        Future outlook and opportunities     28
    • 1.7.1    Emerging trends           28
      • 1.7.1.1 Hybrid Neuromorphic-Conventional Computing and Sensing Systems 28
      • 1.7.1.2 Edge AI and IoT             28
      • 1.7.1.3 Quantum Computing               29
      • 1.7.1.4 Explainable AI                30
      • 1.7.1.5 Brain-Computer Interfaces   31
      • 1.7.1.6 New Application Areas and Use Cases        31
      • 1.7.1.7 Disruptive Business Models and Services  32
      • 1.7.1.8 Collaborative Ecosystem Development       32
      • 1.7.1.9 Skill Development and Workforce Training 33
    • 1.7.2    Technology roadmap 34
  • 1.8        Neuromorphic computing and generative AI             36
  • 1.9        Market value chain     37
  • 1.10     Market map    38
  • 1.11     Funding and investments      39
  • 1.12     Strategic Partnerships and Collaborations 40
  • 1.13     Regulatory and Ethical Considerations        42
    • 1.13.1 Data Privacy and Security      42
    • 1.13.2 Bias and Fairness in Neuromorphic Systems           42
    • 1.13.3 Intellectual Property and Patent Landscape             43
    • 1.13.4 Environmental Impact and Sustainability   43

 

2             INTRODUCTION          45

  • 2.1        Definition and concept of neuromorphic computing and sensing              45
  • 2.2        Main neuromorphic approaches      46
    • 2.2.1    Large-scale hardware neuromorphic computing systems               48
    • 2.2.2    Non-volatile memory technologies 49
    • 2.2.3    Advanced memristive materials and devices           50
  • 2.3        Comparison with traditional computing and sensing approaches             51
  • 2.4        Neuromorphic computing vs. quantum computing             52
  • 2.5        Key features and advantages              53
    • 2.5.1    Low latency and real-time processing          54
    • 2.5.2    Power efficiency and energy savings              54
    • 2.5.3    Scalability and adaptability 54
    • 2.5.4    Online learning and autonomous decision-making             55
  • 2.6        Markets and Applications      55
    • 2.6.1    Edge AI and IoT             57
    • 2.6.2    Autonomous Vehicles and Robotics              59
    • 2.6.3    Cybersecurity and Anomaly Detection         61
    • 2.6.4    Smart Sensors and Monitoring Systems      62
    • 2.6.5    Datacenter and High-Performance Computing      63

 

3             NEUROMORPHIC COMPUTING TECHNOLOGIES AND ARCHITECTURE               66

  • 3.1        Spiking Neural Networks (SNNs)      67
    • 3.1.1    Biological inspiration and principles              68
    • 3.1.2    Types of SNNs and their characteristics      69
    • 3.1.3    Advantages and limitations of SNNs              69
  • 3.2        Memory Architectures for Neuromorphic Computing        70
    • 3.2.1    Conventional memory approaches (SRAM, DRAM)              70
    • 3.2.2    Emerging non-volatile memory (eNVM) technologies         70
      • 3.2.2.1 Phase-Change Memory (PCM)           71
      • 3.2.2.2 Resistive RAM (RRAM)             72
      • 3.2.2.3 Magnetoresistive RAM (MRAM)          72
      • 3.2.2.4 Ferroelectric RAM (FeRAM)   72
    • 3.2.3    In-memory computing and near-memory computing         73
    • 3.2.4    Hybrid memory architectures             74
  • 3.3        Neuromorphic Hardware and Processors  75
    • 3.3.1    Digital neuromorphic processors     75
    • 3.3.2    Analog neuromorphic processors    76
    • 3.3.3    Mixed-signal neuromorphic processors       76
    • 3.3.4    FPGA-based neuromorphic systems             77
    • 3.3.5    Neuromorphic accelerators and co-processors     77
  • 3.4        Software and Frameworks for Neuromorphic Computing               78
    • 3.4.1    Neuromorphic programming languages and tools               79
    • 3.4.2    Neuromorphic simulation platforms and frameworks       79
    • 3.4.3    Neuromorphic algorithm libraries and repositories              80
    • 3.4.4    Neuromorphic software development kits (SDKs) 81

 

4             NEUROMORPHIC SENSING TECHNOLOGIES AND ARCHITECTURES     83

  • 4.1        Event-Based Sensors and Processing           83
    • 4.1.1    Neuromorphic vision sensors             84
    • 4.1.2    Neuromorphic auditory sensors       86
    • 4.1.3    Neuromorphic olfactory sensors      86
    • 4.1.4    Event-driven processing and algorithms     87
  • 4.2        Hybrid Sensing Approaches 88
    • 4.2.1    Combination of conventional and event-based sensors   90
    • 4.2.2    Fusion of multiple sensing modalities          91
    • 4.2.3    Advantages and challenges of hybrid sensing         91
  • 4.3        Neuromorphic Sensor Architectures and Designs                92
    • 4.3.1    Pixel-level processing and computation      92
    • 4.3.2    Sensor-processor co-design and integration            93
    • 4.3.3    Bio-inspired sensor designs and materials                93
  • 4.4        Signal Processing and Feature Extraction Techniques        94
    • 4.4.1    Spike-based Encoding and Decoding            95
    • 4.4.2    Temporal and Spatiotemporal Feature Extraction 96
    • 4.4.3    Neuromorphic Filtering and Denoising         97
    • 4.4.4    Adaptive and Learning-Based Processing  98

 

5             MARKET ANALYSIS AND FORECASTS             100

  • 5.1        Mobile and Consumer Applications               100
    • 5.1.1    Smartphones and wearables              101
    • 5.1.2    Smart home and IoT devices                102
    • 5.1.3    Consumer health and wellness         103
    • 5.1.4    Entertainment and gaming   104
  • 5.2        Automotive and Transportation         107
    • 5.2.1    Advanced Driver Assistance Systems (ADAS)          108
    • 5.2.2    Autonomous vehicles and robotaxis              110
    • 5.2.3    Vehicle infotainment and user experience  112
    • 5.2.4    Smart traffic management and infrastructure          113
  • 5.3        Industrial and Manufacturing             117
    • 5.3.1    Industrial IoT and smart factories     118
    • 5.3.2    Predictive maintenance and anomaly detection    119
    • 5.3.3    Quality control and inspection           121
    • 5.3.4    Logistics and supply chain optimization     122
  • 5.4        Healthcare and Medical Devices      126
    • 5.4.1    Medical imaging and diagnostics     127
    • 5.4.2    Wearable health monitoring devices              128
    • 5.4.3    Personalized medicine and drug discovery                130
    • 5.4.4    Assistive technologies and prosthetics        132
  • 5.5        Aerospace and Defense         135
    • 5.5.1    Unmanned Aerial Vehicles (UAVs) and drones        136
    • 5.5.2    Satellite imaging and remote sensing            138
    • 5.5.3    Missile guidance and target recognition      139
    • 5.5.4    Cybersecurity and threat detection:               140
  • 5.6        Datacenters and Cloud Services      144
    • 5.6.1    High-performance computing and scientific simulations:              144
    • 5.6.2    Big data analytics and machine learning     146
    • 5.6.3    Cloud-based AI services and platforms       148
    • 5.6.4    Energy-efficient datacenter infrastructure 149
  • 5.7        Regional Market Analysis and Forecasts     153
    • 5.7.1    North America              153
    • 5.7.2    Europe                154
    • 5.7.3    Asia-Pacific    156
    • 5.7.4    Rest of the World         158
  • 5.8        Competitive Landscape and Key Players     160
    • 5.8.1    Overview of the Neuromorphic Computing and Sensing Ecosystem        160
    • 5.8.2    Neuromorphic Chip Manufacturers and Processors           160
    • 5.8.3    Neuromorphic Sensor Manufacturers          161
    • 5.8.4    Emerging Non-Volatile Memory (eNVM) Manufacturers    162
    • 5.8.5    Neuromorphic Software and Framework Providers              163
    • 5.8.6    Research Institutions and Academia             164

 

6             COMPANY PROFILES                167  (141 company profiles)

 

7             RESEARCH SCOPE & METHODOLOGY        310

 

8             REFERENCES 311

 

List of Tables

  • Table 1. Overview of the neuromorphic computing and sensing market.               17
  • Table 2. Global market for neuromorphic computing and sensors, 2024-2035 (Millions USD).              19
  • Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2035.      20
  • Table 4. Key market trends and growth drivers.       25
  • Table 5. Market challenges and limitations.              27
  • Table 6. Neuromorphic computing and generative AI strategies. 36
  • Table 7. Funding and investments in neuromorphic computing and sensing.    39
  • Table 8. Strategic Partnerships and Collaborations in the Neuromorphic Industry.        40
  • Table 9. Regulatory and Ethical Considerations of neuromorphic computing & sensing.           43
  • Table 10. Main neuromorphic sensing approaches.            46
  • Table 11. Main Neuromorphic Computing Approaches.   46
  • Table 12. Resistive Non-Volatile Memory (NVM) Technologies.    49
  • Table 13. Advanced Memristive Materials, Devices, and Novel Computation Concepts.            50
  • Table 14. Comparison with traditional computing and sensing approaches.      51
  • Table 15. Comparison between neuromorphic and quantum computing.            52
  • Table 16. Key features and advantages of neuromorphic computing and sensing.         53
  • Table 17. Markets and Applications of neuromorphic computing and sensing. 55
  • Table 18. Von neumann architecture versus neuromorphic architecture.             66
  • Table 19. Types of SNNs and their characteristics.               69
  • Table 20. Advantages and limitations of SNNs.       70
  • Table 21. Conventional memory approaches (SRAM, DRAM).      70
  • Table 22. Emerging non-volatile memory (eNVM) technologies.  71
  • Table 23. Hybrid memory architectures.      74
  • Table 24. Neuromorphic accelerators and co-processors.             78
  • Table 25. Neuromorphic programming languages and tools.        79
  • Table 26. Neuromorphic simulation platforms and frameworks. 80
  • Table 27. Neuromorphic algorithm libraries and repositories.      81
  • Table 28. Neuromorphic software development kits (SDKs).         82
  • Table 29. Hybrid sensing approaches.          89
  • Table 30. Advantages and challenges of hybrid sensing.  91
  • Table 31. Bio-inspired sensor designs and materials.         94
  • Table 32. Signal Processing and Feature Extraction Techniques. 95
  • Table 33. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application.    101
  • Table 34. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application.    102
  • Table 35. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application.    103
  • Table 36. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application.    105
  • Table 37. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD.           105
  • Table 38. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application. 109
  • Table 39. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application.           111
  • Table 40. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.        113
  • Table 41. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.        114
  • Table 42. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD. 115
  • Table 43. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.    118
  • Table 44. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.    120
  • Table 45. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application.    122
  • Table 46. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application.     123
  • Table 47. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD. 124
  • Table 48. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application.    128
  • Table 49. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application.    129
  • Table 50. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application.            131
  • Table 51. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application.       133
  • Table 52. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD.           134
  • Table 53. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application.        137
  • Table 54. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application.               138
  • Table 55. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application.       140
  • Table 56. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application.    141
  • Table 57. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD.              142
  • Table 58. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application. 145
  • Table 59. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application.               147
  • Table 60. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application.          148
  • Table 61. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application.  150
  • Table 62. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD.              151
  • Table 63. Market revenues for neuromorphic computing and sensing by region from 2024-2035 in millions USD. 153
  • Table 64. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD. 153
  • Table 65. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD. 155
  • Table 66. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2035), millions USD. 157
  • Table 67. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2035), millions USD.  158
  • Table 68. Neuromorphic Chip Manufacturers and Their Product Offerings.         161
  • Table 69. Neuromorphic Sensor Manufacturers and Their Product Offerings.    162
  • Table 70. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings.              162
  • Table 71. Neuromorphic Software and Framework Providers and Their Solutions.         163
  • Table 72. Key Research Institutions and Academia in Neuromorphic Computing and Sensing.             164
  • Table 73. Evolution of Apple Neural Engine.              179
  • Table 74. Dynex subscription plans.               205

 

List of Figures

  • Figure 1. Global market for neuromorphic computing and sensors, 2023-2035 (Millions USD).            19
  • Figure 2. Neuromorphic Computing and Sensing Market Segmentation 2020-2035.    21
  • Figure 3. Neuromorphic computing and sensing technology roadmap. 35
  • Figure 4. Market value chain for neuromorphic computing and sensing.               37
  • Figure 5. Neuromorphic computing and sensing market map.     39
  • Figure 6. Evolution of the main hardware technologies for neuromorphic computing. 48
  • Figure 7. Key materials in NVM technology for neuromorphic computing.            50
  • Figure 8. Advanced memristive materials for neuromorphic computing.              51
  • Figure 9. Neural networks in autonomous vehicles.            56
  • Figure 10.  Concept illustration of centralized and decentralized intelligence in robotics.         60
  • Figure 11.  Neuromorphic programmable robot with dynamic vision developed by SynSense.               61
  • Figure 12. Comparison of High-Level Conventional and Neuromorphic Memory Architectures.            67
  • Figure 13. Spiking Neural Network (SNN) Structure and Operation.          68
  • Figure 14. IBM TrueNorth Processor.              76
  • Figure 15. Event-Based Sensor Operation and Data Processing Flow.     84
  • Figure 16. Conventional sensor vs. Event-based sensor.  84
  • Figure 17. Operation of neuromorphic vision sensors.       85
  • Figure 18. Cyranose 320 Electronic Nose.  87
  • Figure 19. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information.              89
  • Figure 20. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications.  100
  • Figure 21. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2035), millions USD.           106
  • Figure 22. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation.             108
  • Figure 23. Sensors used by the ADAS (Advanced Driver-Assistance System).    108
  • Figure 24. Enabling technologies for autonomous vehicles.           110
  • Figure 25. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing.    111
  • Figure 26. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2035), millions USD. 116
  • Figure 27. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing.            118
  • Figure 28. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2035), millions USD. 125
  • Figure 29. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices.             127
  • Figure 30. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities.        129
  • Figure 31. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics.       132
  • Figure 32. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2035), millions USD.           134
  • Figure 33. Technology roadmap for neuromorphic computing and sensing in aerospace and defense.                136
  • Figure 34. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight.    136
  • Figure 35. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2035), millions USD.              143
  • Figure 36. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services.           144
  • Figure 37. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2035), millions USD.              152
  • Figure 38. Global Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2035), millions USD.  154
  • Figure 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2035), millions USD. 156
  • Figure 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, Asia-Pacific (2024-2035), millions USD. 158
  • Figure 41. Global Neuromorphic Computing and Sensing Market Size and Forecast, in in the Rest of the World (2024-2035), millions USD.   159
  • Figure 42. Neuromorphic Computing and Sensing Ecosystem Overview.             160
  • Figure 43. Cerebas WSE-2.   194
  • Figure 44. DeepX NPU DX-GEN1.     203
  • Figure 45. Google TPU.            213
  • Figure 46. GrAI VIP.     215
  • Figure 47. Groq Tensor Streaming Processor (TSP).              217
  • Figure 48. DVL-5000 neuromorphic laser profiler. 224
  • Figure 49. Spiking Neural Processor               227
  • Figure 50. TROOPER robot.   228
  • Figure 51. 11th Gen Intel® Core™ S-Series. 230
  • Figure 52.  Intel Loihi 2 chip. 231
  • Figure 53. Envise.        237
  • Figure 54. Pentonic 2000.      241
  • Figure 55. Azure Maia 100 and Cobalt 100 chips.  245
  • Figure 56. Mythic MP10304 Quad-AMP PCIe Card.              249
  • Figure 57. Nvidia H200 AI chip.           259
  • Figure 58. Grace Hopper Superchip.               260
  • Figure 59. Prophesee Metavision starter kit – AMD Kria KV260 and active marker LED board. 269
  • Figure 60. Cloud AI 100.         271
  • Figure 61. Overview of SpiNNaker2 architecture for the ”SpiNNcloud” cloud system and edge systems.                290
  • Figure 62. Untether AI chip.  301

 

 

The Global Market for Neuromorphic Computing and Sensing 2024-2035
The Global Market for Neuromorphic Computing and Sensing 2024-2035
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