The Global Market for Neuromorphic Computing and Sensing 2024-2035: New Application Areas and Use Cases, Disruptive Business Models, Edge AI, IoT, Quantum Computing, Explainable AI - ResearchAndMarkets.com

The "The Global Market for Neuromorphic Computing and Sensing 2024-2035" report has been added to ResearchAndMarkets.com's offering.

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.

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. 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 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.

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.

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
• Voxel Sensors

Key Topics Covered:

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

2 INTRODUCTION

2.1 Definition and concept of neuromorphic computing and sensing

2.2 Main neuromorphic approaches

2.3 Comparison with traditional computing and sensing approaches

2.4 Neuromorphic computing vs. quantum computing

2.5 Key features and advantages

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

3.2.3 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

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

5.2 Automotive and Transportation

5.3 Industrial and Manufacturing

5.4 Healthcare and Medical Devices

5.5 Aerospace and Defense

5.6 Datacenters and Cloud Services

5.7 Regional Market Analysis and Forecasts

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

For more information about this report visit https://www.researchandmarkets.com/r/2a1n5y

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