Self-Learning Neuromorphic Chip Market To Grow At USD 2.76 Billion at a CAGR of 26.23% by 2030 - Report by Market Research Future (MRFR)


Self-Learning Neuromorphic Chips Accelerate Energy-Efficiency and Robotics

New York, US, Aug. 11, 2022 (GLOBE NEWSWIRE) -- According to a comprehensive research report by Market Research Future (MRFR), “Self-Learning Neuromorphic Chip Market Analysis by Application, by Vertical – Forecast 2030” valuation is poised to reach USD 2.76 Billion by 2030, registering an 26.23% CAGR throughout the forecast period (2022–2030).

Self-Learning Neuromorphic Chip Market Overview

Vast applications of these chips in aerospace & defense, IT & telecom, and automotive sectors drive the market growth. Besides, increased funding & support from the public and private organizations to develop and improve self-learning neuromorphic chips is a key driving force. Rising investments by major OEMs in developing AI-powered devices to be used in healthcare and manufacturing sectors, boost the market size.

Self-Learning Neuromorphic Chip Market Report Scope:

Report MetricDetails
Revenue Forecast By 2030 USD 2.76 Billion
Market Growth 2022 - 2030 26.23%
Base Year 2021
Forecast Period 2022-2030

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With neuromorphic computing going mainstream, the market is making heady strides. Neuromorphic computing technology is transforming industrial technologies significantly. Therefore, venture capitalists and government bodies pour large investments to improve the technology further. Neuromorphic computing platforms are increasingly used across industries to boost speed, performance, and scalability.

Over the past few years, neuromorphic chips have evolved significantly in terms of hardware, software, and performance improvements. Various researches are ongoing to achieve low-power, low-cost, and miniaturized, high-performing neuromorphic chips that can be used in personal health, energy, and traffic management.

For example, a robotic arm based on neuromorphic chip technology could aid in improving spinal movements and postures of neuromuscular injury patients. Also, neuromorphic chips integrated into sensors are used to identify a particular heart rate change leading to heart abnormalities. Neuromorphic chips enable more customized and patient-centric monitoring to recognize slightly different ECG patterns varying between individuals.

Neuromorphic architectures hold a huge potential in deploying machine learning algorithms of the future, enhancing the overall learning performance for specific tasks and algorithms that can learn in real-time, just like biological brains. Vast deployments of machine learning capabilities would further drive the 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 recorded high-frequency oscillations (HFOs) using an intracranial electroencephalogram (iEEG). It has proven to be a promising biomarker for identifying the brain tissue that causes epileptic seizures.

At the same time, significant energy demand from industrial sectors worldwide makes the self-learning neuromorphic chip market outlook appear hugely positive. Also, the rising need for rapid data processing and big data across verticals creates significant market demand. AI and neuromorphic chips mimic and sometimes even outperform human intelligence.

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Self-Learning Neuromorphic Chip Market Segments

The self-learning neuromorphic chip 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 region segment is sub-segmented into the Asia-Pacific, Middle East & Africa, Americas, Europe, and rest-of-the-world.

Self-Learning Neuromorphic Chip Market Regional Analysis

North America dominates the global self-learning neuromorphic chip market. The large presence of major technology providers, such as Intel Corporation and IBM Corporation, positively impacts the region’s market shares. Besides, the augmenting demand for AI-based applications and advanced analytics platforms drive the market growth in the region.

Booming industry verticals like aerospace & defense, IT &telecom, automotive, medical, and manufacturing in the region boost the market size. Mexico, Canada, and the US are major self-learning neuromorphic chip markets in the region.

Europe is another developed market for self-learning neuromorphic chips. The growing adoption of AI, machine learning, and IoT-based systems, alongside the rising demand for better efficiency and productivity of industrial processes, substantiates market revenues. Additionally, the rise in industries, including aerospace & defense, IT & telecom, and automotive, creates significant opportunities.

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Industry Trends

The automotive, healthcare, aerospace & defense industries create significant market demand. Improvements in AI and ML technologies would bolster market growth in future years. Additionally, the growing adoption of self-learning neuromorphic chips for data mining, signal recognition, and image recognition applications would boost the market size.

Also, increasing applications in machine vision, voice identification, and video monitoring would influence the market growth. The rapidly improving sensors market, growing need for better-performing ICs, and demand for neuromorphic computing are major trends in the industry that define the growing market landscape.

The increasing use of software in different applications would offer robust opportunities for market players. The rising adoption of neuromorphic chips in applications like data modeling, predictive analysis, real-time data streaming, and online learning may offer robust opportunities to market players.

Neuromorphic chips promise many advantages, and the technology also enables a powerful method for creating futuristic Chip hardware and revolutionary AI software. However, many ethical considerations around the usage of these chips are common factors impeding market growth. Also, public perception of the technology is one of the most significant ethical challenges.

Self-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 & acquisitions, and product & technology launches. Leading industry players invest strategically in R&D activities and foster their expansion plans.

For instance, on Oct. 01, 2021, Intel unveiled its Loihi 2 chip, the second-generation processor useful in conventional electronics with the architecture of human brains. This innovation is expected to inject new progress into the computing industry and help Intel advance its manufacturing technology. The chip also is a key product for Intel to retrieve its processor manufacturing ability.

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Dominant Key Players on Self-Learning Neuromorphic Chip Market Covered are:

  • Qualcomm (US)
  • Numenta (US)
  • Samsung Group (South Korea)
  • IBM (US)
  • Hewlett Packard (US)
  • Brainchip Holdings Ltd. (US)
  • HRL Laboratories (US)
  • Applied Brain Research Inc. (US)
  • General Vision (US)
  • Intel Corporation (US)

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