Neuromorphic Hardware Market Size, Share and Trends 2025 to 2034

What is the Neuromorphic Hardware Market Size?

The global neuromorphic hardware market is driven by advances in semiconductor design, spiking neural networks, and sensor integration. It allows adaptive learning and ultra-low power consumption for AI-driven devices. The market growth is attributed to rising demand for energy-efficient, brain-inspired processors that enable real-time AI applications in edge and autonomous systems.

Neuromorphic Hardware Market Key Takeaways

• North America dominated the global neuromorphic hardware market with the largest share of 40% in 2024, accounting for an estimated 40% market share.
• Asia Pacific is expected to grow at a notable CAGR from 2025 to 2034.
• By component, the processors/chips segment held the major market share of 60% in 2024.
• By component, the memory & storage (memristors, RRAM) segment is projected to grow at a CAGR between 2025 and 2034.
• By deployment model, the edge devices segment contributed the biggest share of 50% in 2024.
• By deployment model, the cloud-based platforms segment is expanding at a significant CAGR between 2025 and 2034.
• By application, the image & signal processing segment held the maximum market share of 35% in 2024.
• By application, the robotics & autonomous systems segment is expected to grow at a significant CAGR over the projected period.
• By end-user industry, IT & telecom & automotive segment generated the major market share of 45% in 2024.
• By end-user industry, the healthcare & consumer electronics segment is expected to grow at a notable CAGR from 2025 to 2034.

Market Overview

Increased interest in energy-efficient computing is likely to lead to innovation in brain-inspired hardware architectures, resulting in growth in the neuromorphic hardware market. Engineers are recreating neural networks with spiking neural networks and non-volatile memory devices to process data directly on the device itself, rather than accessing a remote server. Planned projects in 2024 include the ENERGIZE consortium under Horizon Europe, which aims to develop neuromorphic hardware using 2D materials as edge devices for chiplet-based in-memory computing. Benchmarked 2D devices for use cases in inference and training, leveraging edge AI.

In 2024, a report by the European Parliament, "Technological Sovereignty and Digital Infrastructure," pointed to the possibility that the power consumption of data centers in the EU would almost triple. EU NEUROPULS Projects Projects like the NEUROPULS target to establish edge-system-based phase-change material and augmented silicon photonics. That develops low-power neuromorphic accelerators with improved security in automotive and Internet of Things ecosystems. Furthermore, the increase in the use of neuromorphic solutions, which are low-latency, reliable, and locally processing-capable solutions for sensitive or mission-critical applications. (Source: https://cordis.europa.eu)

Impact of Artificial Intelligence on the Neuromorphic Hardware Market

Neuromorphic hardware is transforming the computing environment at the pace of adoption of artificial intelligence (AI) in industries. These brain-inspired architectures, unlike conventional processors, process information in parallel, use much less energy, and better handle unstructured data. They are better suited to real-time applications in edge devices, robotics, autonomous vehicles, and healthcare diagnostics. Furthermore, with organizations competing to adopt AI into their daily business, substantial opportunities are being fueled, adding to global competition.

Neuromorphic Hardware Market Growth Factors

• Boosting Advancements in Neuromorphic Memory Devices: Growing progress in memristors, spintronic synapses, and phase-change materials enhances hardware reliability and scalability.
• Rising Deployment in Defense and Aerospace Applications: Expanding investments in AI-driven surveillance, situational awareness, and autonomous drones strengthen demand for neuromorphic systems.
• Driving Adoption in Neurological Research and Healthcare: Increasing use of brain-inspired chips to model cognitive functions accelerates medical research and neuroprosthetics development.
• Fueling Integration with Next-Generation Robotics: Growing reliance on adaptive, low-latency processors supports autonomous decision-making in industrial and service robots.

Market Scope

Market Dynamics

Drivers

How Is Increasing Demand for Energy-Efficient Computing Expected to Shape the Neuromorphic Hardware Market?

Increasing demand for energy-efficient computing is expected to drive market growth. Neuromorphic processors mimic the parallelism of biological neurons and allow orders of magnitude of power reduction over traditional CPUs and GPUs. In 2024, researchers at the University of Buffalo, with the support of the National Science Foundation, emphasized the fact that executing large AI models uses more than 6,000 joules per text response. The human brain works at a steady rate of about 20 joules per second, underscoring the urgency of hardware in the style of the brain.

• In 2024, UC San Diego researchers estimated that AI electricity consumption worldwide will rise by twofold by 2026 and that neuromorphic circuits will be essential as a solution to this growing energy cost. The power consumption of nanoscale neuron circuit designs with side-contacted field-effect diode (S-FED) technology was reported to be only 44 nW, about 85% lower than earlier designs. The syncretism of scholarly advances, industrial research, and policy efforts highlights that neuromorphic hardware is positioned as a decisive capability of sustainable AI computing within the next ten years. Moreover, the rising focus on brain-inspired healthcare solutions is likely to expand the application scope of neuromorphic hardware.

(Source: https://www.buffalo.edu)
Source: https://today.ucsd.edu)
Source: https://www.researchgate.net)

Restraint

High Development Costs Expected to Restrict Commercialization

Market expansion is hindered by high development costs, which are expected to restrict commercialization, in the neuromorphic hardware market. Neuromorphic hardware needs special architectures, new fabrication technologies, and new materials, including memristors and phase-change devices. Small companies have entry barriers, and the product development cycle is long, so it further delays its broad use. Moreover, the hamper widespread deployment through uncertain long-term reliability, which is likely to create adoption risks.

Opportunity

How Are Surging Investments in Autonomous Systems Likely to Accelerate the Growth of the Neuromorphic Hardware Market?

Surging investments in autonomous systems are expected to accelerate, creating immense opportunities for the market in the coming years. Self-driving vehicles, unmanned aerial vehicles, and industrial automation platforms require specialized hardware to handle real-time perception and decision-making in dynamic environments. The neuromorphic designs are capable of analyzing sensor input (radar, lidar, video feeds) at high speed, allowing a more reliable navigation and object recognition.

Technology giants invest a significant amount of money in developing specialized hardware for these applications, while defense-related agencies focus on research into AI-based situational awareness. The growing discipline makes neuromorphic hardware a foundation of the safety and efficiency of autonomous platforms. In 2024, the Loihi-2 chip by Intel was shown to be performing sensor fusion better on radar and visual data than conventional GPU and CPU-based pipelines. Additionally, the autonomous vehicle testing facilities noted that neuromorphic perception systems were able to detect pedestrians and obstacles in poor weather conditions, further facilitating the market in the coming years. (Source: https://www.researchgate.net)

Component Insights

Why Are Processors and Chips Dominating the Neuromorphic Hardware Market?

Processors/chips segment dominated the neuromorphic hardware market in 2024, accounting for an estimated 60% market share, due to the ability to provide the compute density and programmability required by current spiking and hybrid neural models. Well-established foundry systems and development tool chains minimize the risk of integration. Therefore, processor-based architectures are more likely to be used when system vendors roll prototypes into robotics, automotive sensing systems, and industrial controllers.

Large semiconductor companies and research labs have a strong interest in the industry, encouraging talent and spending on chip design. This continues the improvement in performance through iteration and the expansion of supported workloads. In 2024, Intel released information about a massive neuromorphic system and a Loihi-2 study. That had shown orders-of-magnitude improvements in power efficiency and orders-of-magnitude decreases in edge workload on processors, cementing processors as the core. Furthermore, the high focus on edge deployment and real-time decision making is still likely to retain processor offerings as the heart of product roadmaps. (Source: https://newsroom.intel.com)

The memory & storage (memristors, RRAM) segment is expected to grow at the fastest rate in the coming years, as it reduces the data movement and computation to the same substrate. Moreover, these technical strengths are poised to bring memory and storage technologies to the forefront of next-generation neuromorphic architectures.

Development Model Insights

How are Edge Devices leading the Neuromorphic Hardware Market?

Edge devices segment held the largest revenue share in the neuromorphic hardware market in 2024, as they provide near-term deployments with the immediate benefits of reduced latency, privacy, and energy consumption to on-site inference. Additionally, the European CORDIS projects, including ENERGIZE and NEUROPULS, in 2024 prioritized 2D materials, photonic accelerators, and secure edge hardware that enhance the technology pipeline to deployments in the real world. Cloud-based platforms segment is expected to grow at the fastest CAGR in the coming years, owing to the research and training loads are being moved to hybrid neuromorphic-cloud pipelines capable of large-scale model development.

Engineers use cloud resources to train large datasets, coordinate across devices, and manage the lifecycle of spiking/hybrid models and delegate low-latency inferences to edge nodes. This hybrid pattern started to gain traction in 2024 with EU and U.S. research programs targeting scalable neuromorphic testbeds. Furthermore, the experimentation frameworks to connect neuromorphic accelerators to larger AI stacks, and these ecosystem initiatives, are expected to hasten platform adoption.

Application Insights

What Makes Image & Signal Processing the Largest Application Segment in the Neuromorphic Hardware Market?

The image & signal processing segment dominated the neuromorphic hardware market in 2024, as event-driven sensors and event-driven processors will have a better temporal resolution and more energy-efficient operation. Neuromorphic event cameras are used with neuromorphic spiking processors to record motion and compress redundant information in a frame.

• In 2024, Fraunhofer exhibitions revealed analog and mixed-signal inference accelerators. That minimizes data movement and provides ultra-low latency to on-device signal processing. Moreover, the peer-reviewed papers reported femtojoule-equivalent energy per event on prototype pipelines aimed at embedded medical imaging and industrial inspection is expected to facilitate the segment growth. (Source: https://www.iis.fraunhofer.de)

The robotics & autonomous systems segment is expected to grow at the fastest rate in the coming years, owing to the event-based perception and low-power spiking processors allow physically powerful, real-time sensor fusion in shifting conditions. Radar, lidar, and event-camera streams are mapped to neuromorphic pipelines by developers to enable lower end-to-end latency and more responsiveness during high-velocity maneuvers.

• In 2024, defense and aerospace initiatives, such as FENCE and mBRAIN at DARPA, proposed event-based image projects and sensor-processor co-design. That reported 100x data-reduction factors on sparse scenes in some cases and provided tight feedback loops. Furthermore, the academic industry projects confirmed spiking-based sensor fusion on driving and UAV data, demonstrating stable false positives in detecting obstacles in poor weather and lighting conditions. (Source: https://www.darpa.mi)

End-use Industry Insights

Why Do IT & Telecom and Automotive Account for the Largest Share in the Neuromorphic Hardware Market?

The IT/telecom and automotive segment held the largest revenue share in the neuromorphic hardware market in 2024, due to the urgent requirements for low-latency, energy-efficient processing equipment in network infrastructure and vehicular sensor stacks.

In 2024, major carriers funded preliminary integrations of neuromorphic inference testbeds with 5G/6G edge services. Additionally, the standards organizations and industry consortia also drove interoperability profiles in 2024, which facilitated integration of neuromorphic modules into traditional ECUs and network fabrics and expedited adoption of suppliers.

The healthcare & consumer electronics segment is expected to grow at the fastest CAGR in the coming years, as neuromorphic designs show low-power inference with real-time and wearable diagnostics, implantable sensors, and always-on consumer products. By 2024, clinical centers and NIH-funded efforts will focus on neuromorphic prototypes to analyze biosignals. That allows detection of seizures on devices, with millisecond-resolution response time and energy per event being 100 times lower compared to a state-of-the-art system, has been tested in clinical settings. Furthermore, the increasing trend of productization by semiconductor companies and medical device collaborators is expected to drive the market in the years to come. (Source: https://pubmed.ncbi.nlm.nih.gov)

Regional Insights

How Is North America Maintaining Its Leadership in the Neuromorphic Hardware Market?

North America led the neuromorphic hardware market, capturing the largest revenue share in 2024, due to the federal funding, legacy semiconductor supply chains, and deep research ecosystems to speed up the prototype-to-deployment times. In 2024, supported by national laboratories and federal initiatives and neuromorphic processors were evaluated at low power, performing event-based vision tasks, which reinforced integration cases in telecom and automotive edge stacks.

• In 2024, Intel assembled Hala Point, a system that packages 1,152 Loihi-2 processors and supports more than 1.15 billion neurons and 128 billion synapses. That operates at a peak power of approximately 2,600 W, and the experiment is intended to give system integrators a way to scale and benchmark energy. Moreover, such massive and replenishable demonstrations in 2024, in collaboration with cross-sectoral partners, are expected to maintain North America's lead in near-term demonstrations. (Source: https://newsroom.intel.com)

The Asia Pacific is anticipated to grow at the fastest rate in the market during the forecast period, owing to large-scale national projects, high new fab capacity development, and intense research and development by the private sector in China, Japan, South Korea, and Taiwan. Governments and leading technology companies joined flagship initiatives of AI-chips and municipal subsidy programs, which focused on AI combat infrastructure at the domestic level, further facilitating the market.

Testveda, formed by regions and cross-border consortia, facilitated experiments of sensor-fusion stacks in real-life scenarios across varied environmental conditions, and this brought in systems integrators and shortened piloting in OEM. Furthermore, the investment in R&D is expected to change the shift in long-term manufacturing and deployment impact in the Asia-Pacific after the narrowly focused activity of 2023-2024.

Neuromorphic Hardware Market Companies

• aiCTX AG
• Applied Brain Research Inc.
• BrainChip Holdings Ltd.
• General Vision Inc.
• GrAI Matter Labs
• Hewlett Packard Enterprise (HPE)
• HRL Laboratories, LLC
• IBM Corporation
• Innatera Nanosystems B.V.
• Intel Corporation
• Knowm Inc.
• Micron Technology, Inc.
• Nepes Corporation
• Numenta, Inc.
• Prophesee SA
• Qualcomm Technologies, Inc.
• Samsung Electronics Co., Ltd.
• SK Hynix Inc.
• SynSense AG
• Vicarious AI

Segments Covered in the Report

By Component

• Processors/Chips (Analog, Digital, Mixed-Signal)
• Memory & Storage (Memristors, RRAM, PCM, Spintronic)
• Sensors & Supporting Hardware (Vision sensors, Auditory sensors, Interfaces)
• Software & Tools (Development frameworks, APIs, Simulation tools)

By Deployment Model

• Edge Devices
• On-Premises Data Centers
• Cloud-Based Platforms

By Application

• Image & Signal Processing
• Natural Language Processing (NLP)
• Robotics & Autonomous Systems
• Cybersecurity & Edge AI
• Healthcare & Medical Imaging
• Industrial Automation
• Others (Smart Mobility, Aerospace, Defense)

By End-Use Industry

• IT & Telecom
• Automotive & Transportation
• Healthcare & Life Sciences
• Consumer Electronics
• Aerospace & Defense
• Industrial & Manufacturing
• Others (Education, Research Institutes)

By Region

• North America
• Europe
• Asia Pacific
• Latin America
• Middle East & Africa