New York, United States, Aug. 11, 2022 (GLOBE NEWSWIRE) — According to a comprehensive research report by Market Research Future (MRFR), “Self-Learning Neuromorphic Chips Market Analysis by Application, by Vertical – Forecast 2030” is about to reach USD 2.76 billion by 2030, registering a CAGR of 26.23% throughout the forecast period (2022-2030).
Self-Learning Neuromorphic Chips Market Overview
Extensive applications of these chips in Aerospace & Defense, IT & Telecom, and Automotive industries are driving the growth of the market. Additionally, increased funding and support from public and private organizations to develop and improve self-learning neuromorphic chips is a key driving force. Growing investments by major OEMs in the development of AI-powered devices for use in the healthcare and manufacturing sectors are increasing the size of the market.
Scope of the Self-learning Neuromorphic Chips Market Report:
|Revenue forecasts by 2030||$2.76 billion|
|Market Growth 2022 – 2030||26.23%|
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With the generalization of neuromorphic computing, the market is progressing rapidly. Neuromorphic computing technology is dramatically transforming industrial technologies. Therefore, venture capitalists and government agencies are making large investments to further improve the technology. Neuromorphic computing platforms are increasingly being used across industries to increase speed, performance, and scalability.
Over the past few years, neuromorphic chips have come a long way in terms of hardware, software, and performance improvements. Various researches are underway to obtain high-performance, low-power, low-cost, and miniaturized neuromorphic chips that can be used in personal health, energy, and traffic management.
For example, a robotic arm based on neuromorphic chip technology could help improve spinal movements and postures in patients with neuromuscular damage. Additionally, neuromorphic chips embedded in sensors are used to identify a particular heart rate change leading to cardiac abnormalities. Neuromorphic chips allow for more personalized, patient-centric monitoring to recognize slightly different ECG patterns that vary between individuals.
Neuromorphic architectures hold enormous potential in deploying the machine learning algorithms of the future, improving overall learning performance for specific tasks and algorithms capable of learning in real time, just like biological brains. Wide deployments of machine learning capabilities would further stimulate interest in neuromorphic computing.
The neuromorphic engineering approach is used to reliably and accurately recognize complex biosignals. Self-learning neuromorphic chip technology is used to successfully detect high frequency oscillations (HFOs) recorded using an intracranial electroencephalogram (iEEG). It has proven to be a promising biomarker for identifying the brain tissue responsible for epileptic seizures.
At the same time, the large energy demand from industrial sectors across the globe makes the outlook for the self-learning neuromorphic chips market overwhelmingly positive. Additionally, the growing need for fast data processing and big data across verticals is creating a significant demand in the market. AI and neuromorphic chips mimic and sometimes even surpass human intelligence.
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Self-Learning Neuromorphic Chip Market Segments
The self-learning neuromorphic chips market is segmented into applications, verticals, and regions. The application segment is sub-segmented into data mining, signal recognition, image recognition and others. The vertical segment is sub-segmented into power & energy, media & entertainment, smartphones, healthcare, automotive, consumer electronics, aerospace, defense and others. The regional segment is sub-segmented into Asia Pacific, Middle East & Africa, Americas, Europe and Rest of the World.
Regional Analysis of the Self-Learning Neuromorphic Chips Market
North America dominates the global market for self-learning neuromorphic chips. The significant presence of major technology vendors, such as Intel Corporation and IBM Corporation, is having a positive impact on market shares in the region. Additionally, rising demand for AI-based applications and advanced analytics platforms is driving the market growth in the region.
Booming verticals such as aerospace and defense, IT and telecommunications, automotive, medical, and manufacturing in the region are increasing the market size. Mexico, Canada and the United States are the major markets for self-learning neuromorphic chips in the region.
Europe is another developed market for self-learning neuromorphic chips. Growing adoption of AI, machine learning, and IoT-based systems, along with growing demand for better efficiency and productivity in industrial processes, is driving the market revenue. Additionally, booming industries including aerospace and defense, IT and telecommunications, and automotive are creating significant opportunities.
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The automotive, healthcare, aerospace and defense sectors are creating significant demand in the market. Improvements in AI and ML technologies would enhance the growth of the market in the coming years. Additionally, increasing adoption of self-learning neuromorphic chips for data mining, signal recognition, and image recognition applications would increase the market size.
Additionally, increasing applications in machine vision, voice identification, and video surveillance would influence the growth of the market. Rapid improvement in the sensor market, growing need for higher performance ICs, and demand for neuromorphic computing are major industry trends defining the landscape of the growing market.
The growing use of software in different applications would provide strong opportunities for market players. The increasing adoption of neuromorphic chips in applications such as data modeling, predictive analytics, real-time data streaming, and e-learning may provide strong opportunities for market players.
Neuromorphic chips promise many benefits, and the technology also enables a powerful method for creating futuristic chip hardware and revolutionary AI software. However, numerous ethical considerations regarding the use of these chips are common factors that hinder the growth of the market. Furthermore, the public perception of technology is one of the most important ethical challenges.
Machine Learning Neuromorphic Chip Market Competitive Analysis
The self-learning neuromorphic chip market is estimated to witness several partnerships alongside other strategic initiatives such as expansion, collaboration, mergers and acquisitions, and product and technology launches. Major industry players are strategically investing in R&D activities and driving their expansion plans.
For example, on October 01, 2021, Intel unveiled its Loihi 2 chip, the second-generation processor useful in conventional electronics with the architecture of the human brain. This innovation is expected to inject new advancements into the computer industry and help Intel advance its manufacturing technology. The chip is also a key product for Intel to recover its processor manufacturing capacity.
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Top Key Players in the Self-Learning Neuromorphic Chips Market Covered Are:
- Qualcomm (US)
- Numenta (USA)
- Samsung Group (South Korea)
- IBM (US)
- Hewlett-Packard (USA)
- Brainchip Holdings Ltd. (United States)
- HRL Laboratories (USA)
- Applied Brain Research Inc. (USA)
- General Vision (US)
- Intel Corporation (USA)
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