Neuromorphic Computing Market Projected To Reach USD 9356.4 Mn By 2032, Driven By Innovations In Low-Power, High-Efficiency Computing Solutions | Research By SNS Insider

(MENAFN- GlobeNewsWire - Nasdaq) The Neuromorphic Computing market is driven by significant improvements in neuromorphic technology that allow for more efficient and smarter AI applications with lower energy requirements.

Pune, Aug. 05, 2024 (GLOBE NEWSWIRE) -- Neuromorphic Computing Market Size Analysis:

“According to the SNS Insider report, the Neuromorphic Computing Market size was valued at USD 86.9 Million in 2023. It is expected to reach USD 9356.4 Million by 2032 and grow at a CAGR of 68.27% over the forecast period of 2024-2032.”

Market analysis

The transformative potential across various industries resulted in driving robust demand for neuromorphic computing market. Neuromorphic computing mimics the neural structures and functioning of the human brain, offering a revolutionary approach to processing information with enhanced efficiency. The capability to do significant computation in real-time without consuming a considerable amount of power is driving this technology adoption and use within sectors like autonomous systems, robotics, advanced artificial intelligence (AI) applications.

The growing integration of neuromorphic systems with AI and machine learning software/utility applications. For example a recent NeuRRAM chip, announced by an international body of researchers in August 2024 highlights the potential for neuromorphic computing to be more precise and energy efficient than it currently is as capable at managing many deep learning applications simultaneously. This advancement is pivotal for applications in defense systems, where rapid, accurate analytical insights are crucial for timely decision-making. The growing adoption of neuromorphic chips in consumer electronics and IoT devices is another driver for the market growth. In January 2023, IBM also introduced an AI chip using a7nm technology that exploits the benefits of high energy efficiency, and SynSense's chips with up to1k frames per second processing capabilities are targeted for real-time applications. SiLC Technologies, which launched the Eyeonic Vision System back in October 2023 as well; offered improvements specifically with respect to range precision and millimeter-level accuracy.

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Major Players Analysis Listed in this Report are:

• General Vision, Inc.
• Samsung Electronics Co., Ltd
• Brain Corporation
• HRL Laboratories LLC
• Knowm Inc.
• BrainChip Holdings Ltd.
• International Business Machines Corporation
• Hewlett Packard Company
• Intel Corporation
• CEA-Leti
• Qualcomm Technologies, Inc
• Vicarious FPC, Inc.
• Applied Brain Research Inc.
• Other Players

Neuromorphic Computing Market Report Scope:

Report Attributes	Details
Market Size in 2023	USD 86.9 Mn
Market Size by 2032	USD 9356.4 Mn
CAGR	CAGR of 68.27% From 2024 to 2032
Base Year	2023
Forecast Period	2024-2032
Historical Data	2020-2022
Key Regional Coverage	North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America)
Key Growth Drivers	. Growing Adoption of Neuromorphic Computing to Transform Internet of Things (IoT) to drive the Market Growth . The ability of neuromorphic systems to process large amounts of data quickly and efficiently supports real-time applications in autonomous vehicles, robotics, and AI-driven systems.

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Detailed Analysis of Neuromorphic Computing Market Segment

By Application

In 2023, image processing segment led the market attributing for over 43% of market share. Image processing dominance driven by the presence of this technology in multiple sectors such as automotive, healthcare and media & entertainment industry. This will make more and richer data available, enabling advanced algorithms to be developed for image recognition so as in computer vision tasks. The adoption of artificial neural network models, which exploit parallel architectures for quick computation has only solidified the importance image processing.

By End-Use

Consumer Electronics held more than 56.5% revenue share in the year 2023 and is expected to lead the neuromorphic computing market during forecast period. Consumer trends that favor smaller, more efficient products are driving growth in this category. The proliferation of neuromorphic chips in automotive applications, including autonomous driving and advanced driver-assistance systems (ADAS), is expanding the industry.

Neuromorphic Computing Market Key Segmentation:

By Component

• Hardware
• Software

By Application

• Signal Processing
• Image Processing
• Data Processing
• Object Detection
• Others

By Deployment

• Edge
• Cloud

By End-Use

• Consumer Electronics
• Automotive
• Healthcare
• Military & defiance
• Others

North America Region dominated the Market

North America led neuromorphic computing market and held more than 36% revenue share in 2023. This region gain lead owing to early adoption of neuromorphic applications. Voice and speech engine quality has improved due to the region's focus on AI powered voice and speech recognition. The growth in the US Department of Defense spending on research and development regarding neuromorphic computing is another factor facilitating advancements across autonomous systems/almost and advanced robotics, thereby propelling North America as a leader. The University of Toronto is also working to optimize this neuromorphic systems better, which could make AI models and smart devices even more efficient.

Recent Developments

• In February 2024, SynSense also integrated iniVation sili-chip team to establish complete high-performance intelligent vision solutions requests for both consumer and industrial markets. Intended for replacements of stand-alone processors, vision sensors and integrated compute-in-sensor devices more effectively through a shared roadmap.The collaboration is focused in areas where advances could affect the broad deployment across robotics, automotive, consumer electronics and aerospace markets.
• Submitted August 9, October 2023 - SiLC Technologies launched the Eyeonic Vision System advanced FMCW LiDAR machine vision solution. system which can have even millimeter level accuracy over more than about 1000 meters using all polarization intensity and also the depth.
• In June 2023, BrainChip Holdings Ltd. partnered with Lorser Industries Inc., to optimize Akida for SDR devices and ITAR restricted systems-including demonstrated benefits in processing throughput speed vs power efficiency.

Key Takeaways

• Drivers include growing adoption in AI and robotics, autonomous systems; advances of neuromorphic chips that improve processing capabilities as well decrease energy consumption.
• Most of the big opportunities exist in varied industries such as defense, healthcare, consumer electronics and automotive where neuromorphic technology help perform real-time processing ensuring smooth data handling.
• North American market is leading the on account of rising early introduction, and huge research investments leaves by driving players while Europe showcase will develop essentially achievement credit to expanding spotlight in development new technology.

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Table of Contents – Major Key Points

1. Introduction

2. Industry Flowchart

3. Research Methodology

4. Market Dynamics

5. Porter's 5 Forces Model

6. Pest Analysis

7. Neuromorphic Computing Market Segmentation, by Component

8. Neuromorphic Computing Market Segmentation, by Application

9. Neuromorphic Computing Market Segmentation, by Deployment

10. Neuromorphic Computing Market Segmentation, by End-Use

11. Regional Analysis

12. Company Profile

13. Competitive Landscape

14. USE Cases and Best Practices

15. Conclusion

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