Neuromorphic Computing Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering (Hardware, Software), By Deployment (Edge Computing, Deploy Computing), By Technology (MMES and Non-MEMS), By End-User (Automotive, Healthcare, Consumer Electronics, Military & Defense and Industrial), By Region and Competition 2021-2031F

Forecast Period	2027-2031
Market Size (2025)	USD 6.41 Billion
CAGR (2026-2031)	22.12%
Fastest Growing Segment	Healthcare
Largest Market	North America
Market Size (2031)	USD 21.26 Billion

Market Overview

The Global Neuromorphic Computing Market will grow from USD 6.41 Billion in 2025 to USD 21.26 Billion by 2031 at a 22.12% CAGR. Neuromorphic computing refers to the design of computer architectures that emulate the biological structure and functioning of the human nervous system, utilizing artificial neurons and synapses to process information through spiking neural networks. The expansion of this market is fundamentally supported by the critical need for energy-efficient processing capabilities that allow autonomous systems to operate effectively at the network edge without reliance on continuous cloud connectivity. Additionally, the growing requirement for real-time cognitive processing and low-latency decision-making in robotics and sensory applications serves as a primary driver, distinct from general industry trends.

Despite these advantages, a significant challenge impeding broader market expansion is the lack of standardized software frameworks and benchmarking metrics, which creates complexity in developing algorithms compatible with these novel hardware structures. This interoperability gap hinders the seamless integration of brain-inspired chips into existing digital ecosystems. However, the underlying demand for advanced computational hardware remains robust. According to the World Semiconductor Trade Statistics (WSTS), in 2025, global sales revenue for logic integrated circuits is projected to grow by 37% year-over-year, a surge largely attributed to the escalating hardware requirements for artificial intelligence workloads.

Key Market Drivers

The surging demand for Artificial Intelligence and Machine Learning applications is necessitating a paradigm shift away from traditional computing architectures, which are increasingly bottlenecked by the energy and latency costs of moving data between separate memory and processing units. This "memory wall" poses a critical barrier to scaling complex neural networks, driving the industry toward neuromorphic designs that colocate memory and computation to emulate biological efficiency. Illustrating the immense throughput potential of this approach, according to Intel Corporation, April 2024, in the 'Intel Builds World’s Largest Neuromorphic System' press release, their Hala Point system demonstrated the capacity to execute 20 quadrillion operations per second, achieving efficiency levels that far surpass conventional central processing units. Such performance allows for the execution of massive AI workloads with significantly reduced power consumption, directly addressing the sustainability challenges inherent in training large-scale models.

Concurrently, the escalating need for energy-efficient edge computing solutions is accelerating the adoption of event-based processing in autonomous devices and sensor networks. Unlike standard processors that consume power continuously, neuromorphic chips operate on an event-driven basis, activating only when relevant changes in data occur, which is vital for battery-constrained environments. The scale of this requirement is vast; according to Ericsson, June 2024, in the 'Ericsson Mobility Report', total cellular IoT connections are projected to reach approximately 4.5 billion by the end of 2025, creating an urgent necessity for hardware capable of local, low-power processing. Reflecting the strong capital flow supporting this hardware transition, according to Bloomberg, November 2024, the brain-inspired chip startup Rain AI is seeking to raise $150 million in a new funding round to further develop energy-efficient semiconductors that can challenge established graphics processing units.

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Key Market Challenges

The lack of standardized software frameworks and benchmarking metrics constitutes a substantial barrier to the commercialization and scalability of the Global Neuromorphic Computing Market. Unlike traditional computing architectures that benefit from mature, universal development environments, neuromorphic systems currently require highly specialized, custom-built algorithms to function effectively. This interoperability gap forces developers to engineer solutions from scratch for distinct hardware iterations, significantly increasing research and development costs while delaying the time-to-market for critical edge and autonomous applications.

Consequently, this fragmentation prevents brain-inspired chips from seamlessly integrating into existing digital ecosystems, isolating the technology despite its performance potential. The disconnect between hardware innovation and software readiness limits widespread adoption, causing the sector to lag behind the broader semiconductor industry's rapid infrastructure expansion. This bottleneck is particularly notable given the massive capital flowing into the wider manufacturing landscape to support AI workloads. According to SEMI, in 2025, global fab equipment spending for front-end facilities was projected to increase to $110 billion, driven principally by the surging demand for artificial intelligence technologies. Without a unified software layer, the neuromorphic market faces difficulty in fully capitalizing on this robust industrial momentum.

Key Market Trends

The incorporation of neuromorphic chips into consumer electronics is emerging as a transformative trend, particularly within the augmented reality (AR) and wearable technology sectors. Unlike traditional processors that struggle with the latency demands of real-time human-machine interaction, brain-inspired architectures are enabling high-speed, low-latency tracking capabilities essential for immersive experiences. This shift is driving the development of smart eyewear that can process rapid biological signals without the power drain associated with standard frame-based cameras. Highlighting this advancement, according to Prophesee, July 2025, in the 'Prophesee’s Event-Based Metavision Sensor Earns Design Win' press release, the aSee Glasses-EVS smart eyewear integrated their neuromorphic sensor to achieve an eye movement sampling rate of up to 1,000 Hz, significantly outpacing standard tracking solutions while maintaining the energy efficiency required for wearable form factors.

Concurrently, the emergence of cloud-based neuromorphic computing platforms is dismantling barriers to entry by providing developers with remote access to specialized hardware for training and prototyping. This trend addresses the critical interoperability gap by allowing engineers to validate spiking neural network models and accelerate software development cycles without the immediate need for physical procurement of scarce neuromorphic chips. These platforms are rapidly evolving to offer superior capabilities; according to BrainChip Holdings Ltd, August 2025, in the 'BrainChip Launches Akida Cloud' press release, the newly introduced Akida 2 architecture available on their cloud platform delivers a four-fold increase in performance and efficiency compared to its predecessor, thereby streamlining the path for deploying advanced AI solutions at the edge.

Segmental Insights

The Healthcare segment is rapidly emerging as the fastest-growing area in the Global Neuromorphic Computing Market. This significant expansion is fueled by the integration of neuromorphic processors into next-generation medical diagnostics and imaging systems. These brain-inspired chips provide the essential low-latency and high-energy efficiency required for battery-powered wearable health monitors and advanced bionic prosthetics. Moreover, their superior pattern recognition capabilities enable healthcare providers to analyze complex physiological data in real time, leading to faster and more precise disease detection. This technological shift addresses the industry's urgent demand for intelligent, autonomous medical devices.

Regional Insights

North America commands the leading position in the Global Neuromorphic Computing Market, supported by a strong concentration of major technology firms and extensive infrastructure. This dominance is driven by significant research and development investments from corporations such as Intel and IBM, which facilitate continuous innovation in chip architecture. Moreover, the region benefits from substantial funding initiatives provided by government institutions like the Defense Advanced Research Projects Agency and the Department of Energy. These strategic commitments accelerate the deployment of neuromorphic systems across industrial and defense applications, thereby securing the market leadership of North America.

Recent Developments

• In October 2024, Prophesee revealed expanded collaborations with several industrial and technology partners, including AMD and Lucid Vision Labs, during the VISION 2024 trade fair. The company showcased a new event-based vision system developed with AMD, specifically the Kria KV260 Vision AI Starter Kit designed to accelerate edge AI development. Additionally, Prophesee highlighted the integration of its Metavision sensors into Lucid Vision Labs' Triton2 camera family, enabling high-speed, low-latency machine vision capabilities for industrial automation. These partnerships aim to broaden the commercial adoption of neuromorphic vision technologies in manufacturing and inspection sectors.
• In September 2024, IBM Research reported that its NorthPole neuromorphic processor had achieved significant new performance milestones in speed and energy efficiency. During testing, the prototype chip demonstrated the ability to run complex artificial intelligence tasks, such as image recognition and large language model inference, with considerably lower latency and power consumption than comparable graphics processing units (GPUs). The NorthPole architecture eliminates the traditional separation between memory and processing by integrating them on the same chip, inspired by the human brain's neural structure. These results highlight the potential of neuromorphic computing to reduce the energy costs associated with modern AI operations.
• In April 2024, Intel Corporation announced the construction of Hala Point, a large-scale neuromorphic system initially deployed at Sandia National Laboratories. This system utilizes the company's Loihi 2 processors and supports over 1.15 billion neurons and 128 billion synapses, making it the world's largest neuromorphic system at the time of its release. Hala Point is designed to support research into brain-inspired artificial intelligence and address challenges related to the efficiency and sustainability of large-scale AI models. The system demonstrates substantial improvements in processing speed and energy efficiency compared to conventional computing architectures for specific real-time workloads.
• In January 2024, Innatera Nanosystems launched the Spiking Neural Processor T1, a neuromorphic microcontroller designed for power-constrained sensor-edge applications. The company unveiled the processor at the Consumer Electronics Show (CES), demonstrating its ability to process sensory data using event-driven spiking neural networks. The T1 chip mimics the brain’s processing mechanisms to deliver high-performance signal processing and pattern recognition within a sub-milliwatt power envelope. This new product is aimed at always-on sensing use cases in wearable technology, smart home devices, and Internet of Things (IoT) applications, enabling intelligence closer to the sensor.

Key Market Players

• Intel Corporation
• IBM Corporation
• Qualcomm Incorporated
• BrainChip Holdings Ltd.
• Hewlett Packard Enterprise (HPE)
• SynSense AG
• Knowm Inc.
• General Vision Inc.
• Furiosa AI
• Thinci Inc.

By Offering	By Deployment	By Technology	By End-User	By Region
Hardware Software	Edge Computing Deploy Computing	MMES Non-MEMS	Automotive Healthcare Consumer Electronics Military & Defense Industrial	North America Europe Asia Pacific South America Middle East & Africa

Report Scope:

In this report, the Global Neuromorphic Computing Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

• Neuromorphic Computing Market, By Offering:

• Hardware
• Software

• Neuromorphic Computing Market, By Deployment:

• Edge Computing
• Deploy Computing

• Neuromorphic Computing Market, By Technology:

• MMES
• Non-MEMS

• Neuromorphic Computing Market, By End-User:

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

• Neuromorphic Computing Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Asia Pacific
• China
• India
• Japan
• Australia
• South Korea
• South America
• Brazil
• Argentina
• Colombia
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE