Market Outlook:

The anticipated global neuromorphic computing market size is poised to reach USD 139,291 Thousand by 2024, with a projected escalation to USD 1,300,575 Thousand by 2032, reflecting a compound annual growth rate (CAGR) of 29.5% during the forecast period (2025-2032). 

Neuromorphic computing is an advanced area in computer and engineering sciences that seeks to develop more efficient and adaptive computing systems by emulating the structure and function of the human brain. Unlike traditional computing systems that separate memory and processing units, which creates speed and energy bottlenecks, neuromorphic systems attempt to replicate the neural networks of the brain. These subsystems contain artificial neurons and synapses, which are capable of simultaneously storing and processing information, which makes real-time learning, adaptive behavior, and massive parallelism possible. An important aspect of neuromorphic computing is the incorporation of spiking neural networks (SNNs), which emulate the firing of electrical impulses by biological neurons which allowing the system to work in an event-driven and asynchronous manner. It consumes remarkably less energy while boosting processing speed, which is advantageous for edge computing, robotics, autonomous vehicles, and smart sensors.

Market Dynamics:

Driver:

The growing demand for high-performance ICs drives the growth of the neuromorphic computing market. A semiconductor wafer comprising dozens or millions of small, manufactured resistors, capacitors, and transistors is called an integrated circuit (IC). Depending on its intended use, an integrated circuit (IC) can be either analog or digital. A few of the widely sought-after characteristics of an IC that are significantly propelling the expansion of the neuromorphic computing market are fast processing speed and low power consumption. Although analog circuits are employed to create the architecture of neurons as they tend to closely resemble the human brain, they are noisy and imprecise, making it difficult for them to match the mathematical model of neurons. Nonetheless, the brain operations are highly reliably approximated by the digital circuitry. Digital circuits are perfect for computational neuroscience research that needs discrete-time simulations due to this feature.  Since neuromorphic chips may readily satisfy the need for integrated circuits (ICs) that offer high computing speed and low power consumption, they are the subject of extensive research and development to approach human cognition.

Restraint:

The rising complexity of designing neuromorphic hardware hampers the growth of the neuromorphic computing market. The growing expenses of building neuromorphic chip hardware are one of the main factors restrictive the market for neuromorphic computing. Highly specified architectures, such as spiking neural networks, which are far more complicated than conventional digital circuits, are needed for neuromorphic devices, which are intended to mimic the structure and operation of biological neural networks. This complication necessitates complex circuit designs, cutting-edge materials, and innovative manufacturing techniques, all of which raise research and development (R&D) expenses and time. Scaling the manufacturing of neuromorphic chips to a point where they are economically viable is still a main problem for this nascent technology. The high cost per chip is a result of the present manufacturing procedures not being optimized for large-scale production.

Opportunity:

The increasing adoption of neuromorphic computing solutions in the healthcare sector creates a lucrative opportunity for the growth of the neuromorphic computing market. Neuromorphic technologies transform healthcare by improving treatment outcomes, improving diagnosis accuracy, and optimizing workflow in the medical services.  Faster processing, quicker diagnosis, and increased facility efficiency are the results of integrating neuromorphic circuits with medical imaging systems.  Large datasets may be handled by neuromorphic engineering in neuromorphic hardware, which would also assist radiologists in prioritizing cases and developing new multi-modal imaging apps that would aid in the early detection of illnesses or individualized treatment plans.  Healthcare professionals are looking for new technology paradigms that can increase diagnostic accuracy while guaranteeing patient compliance due to the contemporary demands in the industry for miniaturization, low power consumption, speedy treatments, and non-invasive clinical techniques.

Challenge:

CPUs and GPUs, which make up neuromorphic technology, have special features like synaptic connections and neuron models that may make it difficult to create software that works well across platforms without requiring significant changes. Software development is made more difficult by this diversity since it may call for expertise in areas like hardware-specific optimizations and algorithms influenced by neurology. Several sectors are impacted by the level of software development for neuromorphic technology. It deters developers from utilizing neuromorphic computing to its fullest potential, which limits innovation in applications about cybersecurity, robot autonomy, and health diagnostics.

Key Players:

• Brain Corporation
• CEA-Leti
• General Vision Inc.
• Hewlett Packard Enterprise Development LP
• HRL Laboratories, LLC
• IBM
• Intel Corporation
• Knowm Inc.
• Cognixion
• BrainChip, Inc. 
• MindMaze
• SAMSUNG
• Vicarious
• Bitbrain Technologies
• Qualcomm Technologies, Inc.
• Others

Recent Development:

• In April 2024, Intel Corporation (US) introduced the largest neuromorphic system in the world, Hala Point, at Sandia National Laboratories. It supports brain-inspired AI research and enhances sustainability and efficiency by using Loihi 2 processors, which provide up to 12 times higher performance and more than 10 times more neuron capacity than earlier systems.

• In March 2023, BrainChip, Inc. (Australia) introduced the second-generation Akida platform, which includes Temporal Event-Based Neural Nets (TENN) for high efficiency, Vision Transformers, and sophisticated 8-bit processing. Additionally, it secures Edge AIoT applications, greatly improving smart technology, automotive, healthcare, and industrial performance.

Segment Insights:

By Deployment

The neuromorphic computing market's edge segment is expected to have the biggest market share throughout the course of the forecast period. More intelligent and responsive edge computing solutions will be made possible by the nonstop improvements in neuromorphic engineering, neuromorphic hardware, and neuromorphic software, which will increase the capabilities of edge devices. The second edition of the Akida platform, Akida 2.0, was commercially released by BrainChip (Australia) in October 2023, marking a noteworthy advancement in innovative AI technology.  Upgrades to the new platform comprise optional vision transformer hardware and support for Temporal Event-Based Neural Network (TENN) acceleration. Three distinct versions of the new Akida 2.0 are available: Akida-E for energy efficiency, Akida-S for deep sub-micron microcontroller and SoC integration, and Akida-P for high-performance applications with support for visual transformers.  It is expected that this version would improve edge AI by enabling devices to function more efficiently and independently from cloud systems. The use of edge AI solutions is estimated to rise as companies continue to place a high importance on low latency, energy efficiency, and real-time processing, which will propel further expansion in this market.

By End-Use

It is expected that the consumer electronics sector will hold the largest share for neuromorphic computing throughout the forecast period. Neuromorphic technology is mainly beneficial for smartphones due to its capacity to biometrically identify users. Neuromorphic chips permit the implementation of various complex commands, such as voice-activated control and face identification, on the device itself, diminishing the data transfers to and from the cloud. This reduces the energy expenditure related to processing in the cloud, thereby advancing the expansion of neuromorphic computing. The rising popularity of wearables in personal health and fitness further underscores the importance of the neuromorphic engineering discipline.

Regional Insights:

The North America region is the dominating region for the neuromorphic computing market. Governments in Asia and the Pacific are investing a lot of money in AI technology by giving it significant financing to build AI infrastructure. China’s “Next Generation Artificial Intelligence Development Plan” aims to become a global saviour in AI by 2030 and thus create a conducive environment for the development and application of neuromorphic chips. Incidentally, positive trade policy and other initiatives like China’s “Made in China 2025” program as well as India’s “Digital India” campaign are also driving growth. A ton of money has been invested in research and development of neuromorphic chips, as many entrepreneurs have begun to use them in industries like consumer electronics and healthcare. It is projected that the money will enable the mass manufacturing of its neuromorphic AI-processed smart vision sensor, Speck. It is projected that the money will enable the mass manufacturing of its neuromorphic AI-processed smart vision sensor, Speck. The market for neuromorphic computing is anticipated to grow at a faster rate shortly due to the interest shown by a number of new market participants, who have increased their investments in the emerging technologies.

Segmentation:

By Component

• Hardware
• Software
• Services

By Deployment

• Edge
• Cloud

By Application

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

By End-Use

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

By Region

North America

• USA

• Canada

• Mexico

Europe

• France

• UK

• Spain

• Germany

• Italy

• Rest of Europe

Asia Pacific

• China

• Japan

• India

• South Korea

• Rest of Asia Pacific

Middle East & Africa

• GCC

• South Africa

• Rest of the Middle East & Africa

South America

• Brazil

• Argentina

• Rest of South America

What to Expect from Industry Profile?

1. Save time carrying out entry-level research by identifying the size, growth, major segments, and leading players in the Neuromorphic Computing market in the world.

2. Use PORTER’s Five Forces analysis to determine the competitive intensity and therefore market attractiveness of the Global Neuromorphic Computing market.

3. Leading company profiles reveal details of key Neuromorphic Computing market players’ global operations, strategies, financial performance & recent developments.

4. Add weight to presentations and pitches by understanding the future growth prospects of the Global Neuromorphic Computing market with a forecast for the decade by both market share (%) & revenue (USD Million).

FAQ’s

1) What are the major factors driving the growth of the Global Neuromorphic Computing Market?

• The growing demand for high-performance ICs drives the growth of the neuromorphic computing market. 

2) What would be the CAGR of the Global Neuromorphic Computing Market over the forecast period?

• The Global Neuromorphic Computing Market is poised to grow at a CAGR of 29.5% from 2025 to 2032.

3) Which region will provide more business opportunities for the growth of the Global Neuromorphic Computing Market in the future?

• The Asia Pacific region is expected to create more opportunities in the market.

4) Who are the major players dominating the Global Neuromorphic Computing Market?

• Brain Corporation, CEA-Leti, General Vision Inc., Hewlett Packard Enterprise Development LP, HRL Laboratories, LLC, IBM, Intel Corporation, Knowm Inc., Cognixion, BrainChip, Inc., MindMaze, SAMSUNG, Vicarious, Bitbrain Technologies, Qualcomm Technologies, Inc., Others. 

5) What are the segments in the Global Neuromorphic Computing Market?

• By Component, By Deployment, By Application, and By End-Use are the industry key segments considered for the research study.

6) What is the estimated market revenue for the Global Neuromorphic Computing Market in 2032?

• The estimated revenue for the Global Neuromorphic Computing Market in 2032 is USD 1,300,575 Thousand.