Neuromorphic Computing Statistics and Facts (2025)

Introduction

Neuromorphic Computing Statistics: Neuromorphic computing is a new technology that works like the human brain. It builds computer systems that copy how our brain’s neurons connect and talk to each other. This helps machines learn, process data, and make decisions like humans. It’s especially useful for things like recognizing images or understanding language.

In the past few years, this technology has become more popular. Many companies are now creating brain-like computer chips and systems. Neuromorphic computing could change how we use AI, robots, and medical tools by making them faster and using less energy.

As research continues, this brain-inspired approach is expected to play a big role in the future of computing.

Editor’s Choice

• According to Precedence Research, the global neuromorphic computing market was worth USD 6.90 billion in 2024 and is expected to grow to USD 8.36 billion by the end of 2025.
• Neuromorphic Computing Statistics claimed that the North American region had the biggest market share in 2024, with 37%, leading the world in this technology.
• The Asia Pacific region is growing fast, with a high growth rate of 22.71%.
• When looking at parts of the market, hardware made up the biggest part, at 80% in 2024.
• Software accounted for 11%, while services (such as support, maintenance, and consulting) accounted for 9%.
• Image processing was the top application for this tech, holding 46% of the market in 2024.

(Reference: precedenceresearch.com)

• In 2025, the size of the neuromorphic computing market in the U.S. was about USD 2.13 billion.
• By 2034, it is expected to grow to nearly USD 12.33 billion.
• This means the market could grow steadily at about 21.49% annually from 2025 to 2034.
• Similarly, the neuromorphic sensing market is expected to reach USD 410 million by 2029 and grow to USD 2.9 billion by 2034.
• Likewise, the neuromorphic computing market is forecasted to increase to USD 412 million by 2029 and USD 5.4 billion by 2034. 

Top Features of Neuromorphic Computing

• Only processes data when changes occur, enhancing energy efficiency.
• Requires significantly less energy than traditional CPUS and GPUS.
• Adapts dynamically to new information without retraining on large datasets.
• Simulates massive parallelism akin to the human brain’s neural networks.
• Maintains functionality despite hardware failures, mimicking the brain’s resilience.
• Integrates processing, memory, and communication in a single unit, reducing physical space requirements.
• Easily expands by adding more neuromorphic cores to handle increased workloads.
• Utilises analogue signals for processing, leading to faster and more efficient computations.
• Features synapses that adjust their strength based on activity, similar to biological learning.
• Processes data locally, reducing latency and bandwidth usage in Iot applications.

Neuromorphic Computing Market Size By Market.us

• The global neuromorphic computing market was valued at USD 5.1 billion in 2023 and is projected to reach USD 29.2 billion by 2032.
• The market is expected to grow at a CAGR of 22% from 2023 to 2032.

• The hardware segment held the largest share by component, accounting for 65.56% of total revenue in 2022.
• Edge computing led the deployment type segment with a 67% revenue share in 2022.
• By application, image processing dominated with a 47% market share in 2022.
• Consumer electronics contributed the highest end-use share, accounting for 57% of the market during the forecast period.
• North America is expected to maintain a 40.5% market share throughout the forecast period.
• Key players include Intel Corporation, Qualcomm Inc., IBM Corporation, Samsung Electronics Co. Ltd., HRL Laboratories, Hewlett-Packard, Vicarious, General Vision Inc., Numenta, Knowm Inc., and CEA-Leti.

Market Size By Components

(Reference: market.us)

• Between 2024 and 2032, the neuromorphic computing market saw steady and strong growth. It began at USD 6.1 billion in 2024 and grew yearly, reaching USD 29.2 billion by 2032.
• Over the years, hardware remained the largest part of the market, rising from USD 4 billion to USD 19 billion.
• At the same time, software grew from USD 1.31 billion to USD 6.25 billion, showing increasing demand for AI tools.
• The services segment also expanded, climbing from USD 0.83 billion to USD 3.97 billion.

Market Share By Region

(Reference: precedenceresearch.com)

• As of 2024, North America has the largest share at 37%, meaning it is leading in using and developing this technology.
• Asia Pacific is next with 29%, showing strong growth in countries like China, Japan, and South Korea.
• Europe holds 20%, and is also making steady progress in research and development.
• Latin America, the Middle East, and Africa (MEA) all have 7%, which means they are still growing in this area but have potential for the future.

By Component

(Reference: precedenceresearch.com)

• Hardware (80%): In 2024, this is the largest part of the market. It includes brain-like chips and devices that power neuromorphic systems.
• Software (11%): This part includes programs that control how the neuromorphic hardware works and learns.
• Services (9%): Support tasks like system setup, updates, maintenance, and expert help.

By Company

By Applications

• Neuromorphic Computing Statistics in 2024 stated that the signal processing segment accounted for the highest share, 30%, in the neuromorphic computing market, as it boosts signal speed and efficiency.
• Image Processing (25%): Helps with tasks like recognising images, mimicking the human brain for better results.
• Data Processing (20%): Sorts and analyses large data sets, offering faster, more accurate results.
• Object Detection (25%): Ideal for robotics and self-driving cars due to quick, brain-like decision-making.

By End Use Industry

(Reference: scoop.market.us)

• Neuromorphic Computing Statistics reports that consumer electronics comprise the biggest % of the market at 48%, showing that neuromorphic computing is widely used in smart gadgets to improve performance and user experience.
• The automotive sector holds a 27% share, using this technology in self-driving features, safety systems, and smart car functions.
• Aerospace and defence account for 11% of the total, and they are used in drones, surveillance, and training tools.
• Healthcare has 8%, using it to enhance medical imaging, diagnosis, and personalised care.
• The remaining 6% goes to various other fields, proving that neuromorphic computing is gaining interest across different industries.

Key Specifications Neuromorphic Technologies

• ODIN and µBrain have 256 neurons and 64K synapses per core, with one chip on each board.
• DYNAPS also uses 256 neurons but only 16K synapses per core.
• BrainScaleS increases this to 512 neurons and 128K synapses per core, using four chips per board.
• SpiNNaker holds 36K neurons and 2.8M synapses per core, across 352 chips per board.
• Neurogrid includes 65K neurons and 8M synapses per core, with 56 chips per board.
• Loihi supports 130K neurons and 130M synapses per core, using 16 chips.
• TrueNorth is the largest, with 1M neurons, 256M synapses per core, and 4096 chips per board.

Materials Systems for Advancing Neuromorphic Computing Applications

• Oxides such as HfOx/Tiox/HfOx/Tiox offer fast write speeds in the tens of nanoseconds and excellent data retention for over 15 minutes between 20°C and 85°C.
• VO2 has a quick write speed of 1 second, with good data retention at 70°C, though its memory duration and potentiation are limited.
• Nb2o5/Pt delivers moderate write speeds in the hundreds of nanoseconds but boasts exceptional data retention for over 500 years at room temperature, which is ideal for long-term storage.
• WOX has slower write speeds, ranging from hundreds of nanoseconds to microseconds, but it retains reasonable data for over three hours at room temperature.
• Other materials like Nb-doped a-STO and Pt/Tio2/Pt show varied performance.
• Ferroelectric and magnetic materials show promise but may require external factors for operation.
• Liquid-solid systems, like ionic liquid/SmNiO3, offer unique capabilities with moderate speeds but need extra control circuits.

Neuromorphic Computing and Engineering Statistics

• The journal has an impact factor of 4.4, placing it in the top 10% of similar journals.
• 185 papers have been published, earning around 1,400 citations, within the top 50%.
• Its h-index is 16, and the g-index is 30, both in the top 50%.
• The extended impact factor is also 4.4, ranking in the top 10%.
• There are 213 total documents, and the journal has received 1,600 citations.

(Source: exaly.com)

• In 2022, there were around 25 articles, with very few citations.
• In 2023, articles jumped to nearly 90, and citations peaked at over 550.
• In 2024 the article count increased to 125, but citations dropped slightly to around 450.

Technological Advancements of Neuromorphic Computing

• In 2025, 2D spintronics became a promising technology for neuromorphic computing. It stands out for its extremely low energy use (0.14 femtojoules per operation) and fast switching speeds (less than a nanosecond), making it ideal for creating scalable and energy-efficient systems.
• At the same time, brain-inspired hardware accelerators gained popularity. These devices replicate how the brain works, boosting the performance of artificial intelligence (AI) and machine learning (ML) tasks.
• Partnerships between universities and industry leaders, like Cornell Tech and BrainChip, played a key role in bringing neuromorphic computing into educational programs. This collaboration encouraged innovation and helped develop new talent in the field.

Notable Product Developments of Neuromorphic Computing Statistics

• Intel’s Hala Point Neuromorphic Computer: In April 2024, Intel introduced Hala Point, the largest neuromorphic computer in the world. It features 1,152 Loihi 2 chips, with 1.15 billion artificial neurons and 128 billion synapses, designed to mimic the human brain. This powerful system aims to push forward AI research.
• Cerebras’ AI Inference Tool: In August 2024, Cerebras Systems launched a new AI inference tool to compete with Nvidia’s processors. It uses Cerebras’ Wafer-Scale Engines, which are much larger than typical chips, allowing it to handle large-scale AI data processing more efficiently.
• Google’s TPU v6 (Trillium) was released in October 2024. This sixth version of the Tensor Processing Unit is 4.7 times faster than the previous one, helping to run AI tasks much more efficiently.
• In April 2025, Google introduced its seventh-generation TPU, called Ironwood. This chip is built to handle very large AI workloads. It can be connected into clusters of up to 9,216 chips, offering a huge boost in computing power for advanced AI systems.

Neuromorphic Statistics by AI Computing and Sensing

• The neuromorphic sensing market is projected to grow to USD 5 billion by 2030, with an annual rate of 116% from 2025 to 2030.
• The neuromorphic computing market is expected to reach USD 2 billion by 2030, growing at an annual rate of 88% from 2025 to 2030.
• The main sectors of this technology will be consumer, industrial, and automotive. By 2025, neuromorphic technology in industrial applications will still be a niche market, but combined computing and sensing will reach USD 2 billion by 2030.
• According to Yole Développement (Yole), the global mobile and other consumer applications market will reach USD 2.8 billion by 2030.
• Additionally, neuromorphic computing for automotive applications is set to reach USD 2 billion by 2030.

Conclusion

To sum up, neuromorphic computing is a promising technology that copies how the human brain functions. It helps computers improve their learning, decision-making, and problem-solving. This can lead to smarter gadgets, faster systems, and big improvements in artificial intelligence.

There are still some challenges, like reducing the cost and size of the hardware, but progress is being made. Overall, the future of neuromorphic computing looks bright and may soon become a key part of everyday technology. As research continues, it could revolutionise industries ranging from healthcare to robotics, making technology smarter and more adaptive to human needs.