Neuromorphic Computing Market - Forecast(2025 - 2031)

Neuromorphic Computing Market Overview

The Neuromorphic Computing Market size is estimated to reach $8486.4 Million by 2030, growing at a CAGR of 18.5% during the forecast period 2024-2030.

The market growth is due to the growing preference for artificial neural systems in chatbots, nonlinear controls & robotics, rise in demand for artificial intelligence & cloud computing applications across numerous industries, increased interest in the use of spiking neural networks and so on. North America dominated the global Neuromorphic Computing market with a share of 37% in 2021, attributed to growing utilisation of artificial neural systems for speech processing recognition, government investment in the development of graphic processing units & neuromorphic chips, emergence of spiking neural networks and others. Neuromorphic chips are increasingly being used in the training of cognitive & brain-based robots, as well as within healthcare sector to monitor the progress of the patients followed by advanced medical devices. Moreover, increasing adoption of software with neuromorphic computing capabilities, rising demand for graphic processing units in processing applications and increasing preferences for smart devices have been eventually boosting the adoption of data & signal processing systems. These factors are set to transform the Neuromorphic Computing industry outlook in the coming time.

Neuromorphic Computing Market Report Coverage

The “Neuromorphic Computing Market Report – Forecast (2024-2030)” by IndustryARC, covers an in-depth analysis of the following segments in the Neuromorphic Computing Market.

By Offering: Hardware and Software.
By Deployment Type: Edge Computing and Cloud Computing.
By Technology: Biological Neural Computation and Artificial Intelligence (AI) Neural Networks.
By Application: Image processing, Signal Processing, Data Processing, Object Detection and Others.
By Industry Vertical: Automotive, Consumer Electronics, Aerospace, Military & Defense, IT & Telecommunication, Manufacturing, Medical & Healthcare and Others.
By Geography: North America (U.S, Canada, Mexico), Europe (U.K, Germany, France, Italy, Spain, Others), APAC (China, Japan India, South Korea, Australia, Others), South America (Brazil, Argentina, Others), RoW (Middle East, Africa).

Key Takeaways

• Edge Computing segment is analyzed to grow at the fastest rate in the global Neuromorphic Computing market during the forecast period 2022-202, attributed to increased implementation of processing-intensive applications with AI, IoT & machine learning capabilities, rising integration of edge computing applications & platforms and so on.
• Neuromorphic Computing for processing applications is analyzed to grow with the highest CAGR during 2022-2027, attributed to the growing utilization of efficient processing applications for real-time monitoring, increasing use of drones & smart cameras for surveillance systems and so on.
• North America dominated the global Neuromorphic Computing market in 2021, attributed to growing utilization of artificial neural systems for speech processing recognition, government investment in the development of graphic processing units & neuromorphic chips, the emergence of spiking neural networks and so on.
• Increasing adoption of smart sensors in autonomous vehicles & connected car infrastructure alongside technological advancements in computing leveraging AI or ML algorithms to facilitate high efficiency are analyzed to significantly drive the market growth of the Neuromorphic Computing market during the forecast period 2024-2030.

 Global Neuromorphic Computing Market Value Share, by Region, 2021 (%)

Neuromorphic Computing Market Segment Analysis - by Deployment Type

Edge Computing segment is analyzed to grow at the fastest rate of 19.4% CAGR in the global Neuromorphic Computing market during the forecast period 2022-202, attributed to the rising implementation of processing-intensive applications with AI, IoT & machine learning capabilities, rising integration of edge computing applications & platforms with existing cloud computing architectures and so on. The artificial neural computing framework minimizes the complexity of interconnected systems by enabling real-time data collection and analysis. In March 2022, IBM and FogHorn collaborated to develop a secure and open next-generation hybrid cloud platform with an advanced, closed-loop control system and edge-powered artificial intelligence. The collaboration intends to assist customers in rapidly processing, implementing, evaluating, storing and training critical data by combining edge & cloud capabilities. Edge computing is widely used in areas such as vehicle responsive voice control, onboard intelligence for assistive robotics, and gesture recognition for touchless interfaces, which has resulted in the growth of the edge computing segment within the Neuromorphic Computing industry over time.

Neuromorphic Computing Market Segment Analysis - by Application

Neuromorphic Computing for image processing applications is analyzed to grow with the highest CAGR of 20.5% during 2022-2027, attributed to increasing security concerns in various parts of the world, growing utilization of efficient processing applications for real-time monitoring, increasing use of drones & smart cameras for surveillance systems and so on. In June 2020, CaixaBank launched a project to install facial recognition technology in over 100 ATMs across Spain in order to provide ATM users with touchless payment withdrawal services. Such advancements are expected to increase the use of graphic processing units for processing applications and recognition technology, thereby raising the demand for neuromorphic computing. Furthermore, rising consumer demand for high-bandwidth data services, rapid advances in image recognition technology that enable users to link offline content with digital content such factors are driving up demand for neuromorphic computing. Such factors are analyzed to accelerate the growth of the image processing segment within the Neuromorphic Computing industry in the coming time.

Neuromorphic Computing Market Segment Analysis - by Geography

North America dominated the global Neuromorphic Computing market with a share of 37% in 2021, attributed to growing utilization of artificial neural systems for speech processing recognition, government investment in the development of graphic processing units & neuromorphic chips, the emergence of spiking neural networks and so on. In September 2020, the US Department of Energy announced a USD 2 million grant for five basic research studies aimed at improving neuromorphic computing. This initiative aimed at promoting the development of hardware & software for neural network-based computing. In March 2020, Intel Corp. launched an experimental research system for neuromorphic computing, an advanced method that can simulate the way human brains work in order to perform computations faster with less energy consumption. Rising demand for high-performance, secure computing platforms in space missions, increasing utilization of neuromorphic computing chips in consumer electronics, and presence of major market players namely IBM Corporation, Intel Corporation are expected to drive the adoption of neuromorphic computing technology in the region. 

Neuromorphic Computing Market Drivers

Increasing Adoption of Smart Sensors in Autonomous Vehicles & Connected Car Infrastructure is Accelerating the Growth of Neuromorphic Computing Market:

The growing utilization of smart sensors in autonomous vehicles and connected car infrastructure is driving the growth of the neuromorphic computing market. Neural networks are being used in automobiles to detect lines, segment vehicle environments, navigate and drive. It facilitates self-driving vehicles with high-performance graphics cards, processors and massive amounts of data, alongside reducing traffic congestion and increasing road safety. Neuromorphic Computing applications in self-driving cars facilitate autonomous decision-making by leveraging data from various sensors like cameras, LiDAR, RADAR, GPS or inertia sensors. This data is then used to train deep learning algorithms to make decisions relevant to the environment of the vehicle. In January 2022, Mercedes adopted neuromorphic computing in the Mercedes Vision EQXX concept car to reduce power consumption by increasing vehicle range. It employs the BrainChip Akida neuromorphic chip to improve the power efficiency of in-cabin keyword detection systems. According to a report published by the Financial Express in 2022, the market penetration of connected cars is expected to rise from around 40% in 2020 to more than 70% in 2025. As stated in the report, the globally connected car market is expected to grow from $56 billion in 2020 to approximately $65 billion in 2021. These factors will eventually help in expanding the Neuromorphic Computing market size in the long run.

Technological Advancements in Computing that Leverage Artificial Intelligence (AI) & Machine learning (ML) Algorithms to Facilitate High Efficiency are Boosting the Growth of the Neuromorphic Computing Market:

Rising technological advancements in computing leveraging AI & ML algorithms drive the growth of the neuromorphic computing market. Neuromorphic computing is widely used in autonomous systems as next-generation intelligent devices since it is guided by biological neural computation principles and uses new algorithmic approaches that mimic the human brain to interact with the environment. They have capabilities that are more similar to human cognition, which allows for their wider application in computing areas. Technical advances like Spiking neural networks dynamically re-map neural networks are used in neuromorphic computing to make decisions in response to discovered patterns. These asynchronous, event-based processors allow neuromorphic computers to outperform traditional architectures by orders of magnitude and improve autonomous AI solutions. In July 2021, Innatera launched the Innatera chip with neuromorphic AI accelerator capabilities to speed up various Spiking neural networks for audio, health and radar applications. The Innatera chip is intended to assist spiking neural networks in pattern recognition tasks by timing spikes in an electrical signal. According to a report published by Accenture Research in 2020, more than 82% of the respondents believe that artificial intelligence will work alongside humans as a colleague, producers, and trusted advisors within the next two years. These factors are set to elevate the growth of the Neuromorphic Computing market size in the coming time. 

Neuromorphic Computing Market Challenges

Slow Processing Speed of the Neuromorphic Processors along with the Lack of Awareness about Neuromorphic Computing is Limiting its Market Growth:

Slow processing speeds of neuromorphic processors, as well as a lack of awareness about neuromorphic computing, are limiting the growth of the market. Numerous factors contribute to the lack of speed of neural networks, including store latency, data shuffling between & within the graphics card pipeline and limited width of the graphics card pipeline. Furthermore, unnecessary precision in most neural network calculations, as well as invariance of input data, contribute to the slow speed of the neural networks. According to the Public Attitudes Towards Robots survey conducted by the European Commission in 2020, over 60% of EU citizens were skeptical about using robots for taking care of their elderly parents as well as children. Thus, robots are underutilized due to a lack of awareness and trust. These factors are impeding the growth of the Neuromorphic Computing industry during the forecast period.

Neuromorphic Computing Industry Outlook

Product launches, acquisitions and R&D activities are key strategies adopted by players in the Neuromorphic Computing Market. The top 10 companies in the Neuromorphic Computing market are:

1. IBM Corporation
2. Intel Corporation
3. Brainchip Holdings Limited
4. Qualcomm Inc.
5. Hewlett Packard Enterprise
6. HRL Laboratories LLC
7. Flow Neuroscience AB
8. Innatera Nanosystems B.V.
9. Aspinity, Inc.
10. Samsung Electronics Limited

Recent Developments

• In November 2021, Qualcomm Technologies Collaborated with Google Cloud in exploring the potential of neural architecture to assist the companies in creating and optimizing AI models. Such collaborations will accelerate the process of automation in various industries.
• In September 2021, Intel launched Loihi, a next-generation neuromorphic processor that mimics the behavior of the neurons. It incorporates basic hardware changes to assist in the execution of new classes of algorithms.
• In April 2021, BrainChip Holdings Ltd. launched MetaTF, a powerful and flexible machine learning platform that facilitates the transition to neuromorphic computing without training. The MetaTF development environment is a simple, fully featured machine learning framework for building, training and testing neural networks. 

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