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Neuromorphic Hardware Market Forecast: What Market Value Is Expected By 2030?
The scale of the neuromorphic hardware market has expanded rapidly over recent years. Projections indicate its expansion from $4.58 billion in 2025 to $5.59 billion by 2026, demonstrating a compound annual growth rate (CAGR) of 22.0%. This historical growth is attributable to several factors, including an escalating demand for low-power computing, the rising implementation of AI workloads, an increasing requirement for real-time data processing, the expanding deployment of edge computing devices, and a burgeoning fascination with brain-inspired architectures.
The neuromorphic hardware market size is anticipated to experience substantial growth in the coming years. It is projected to expand to $12.26 billion by 2030, achieving a compound annual growth rate (CAGR) of 21.7%. This expansion during the forecast period is driven by several factors, including the increasing deployment of artificial intelligence (AI) at the edge, growing integration into autonomous systems, rising demand for energy-efficient processors, expanding applications in robotics and healthcare, and growing investments in neuromorphic technology. Major trends influencing this period include technological advancements in memristive architectures, innovations in spiking neural network designs, developments in event-driven neuromorphic sensors, ongoing research and developments in photonic computing, and progress in hybrid neuromorphic-artificial intelligence (AI) chip integration.
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Neuromorphic Hardware Market Development Factors: Which Trends Are Supporting Demand?
The future growth of the neuromorphic hardware market is anticipated to be significantly propelled by the increasing deployment of autonomous vehicles and robotics. These systems are defined by their capacity to perform tasks with minimal or no human intervention, leveraging advanced sensors, processors, and machine-learning algorithms to perceive, interpret, and respond to their environments. The heightened adoption of autonomous vehicles and robotics is a direct result of the growing demand for automation and enhanced operational efficiency across diverse industries, including transportation, manufacturing, logistics, and defense. Neuromorphic hardware plays a crucial role in enabling this expansion by mimicking the human brain’s neural architecture, thereby facilitating ultra-low-latency processing, real-time learning, low-power computation, and adaptive intelligence – capabilities that traditional processors struggle to achieve. For instance, the International Federation of Robotics, a Germany-based industry association, reported in September 2025 that China recorded 2,027,000 industrial robots working in factories in 2024, up from just over 1,890,000 units in 2023. Annual installations reached 295,000 units, marking a 7% increase from the previous year, representing 54% of global demand and the highest level ever recorded. Therefore, the expansion of autonomous vehicles and robotics is indeed a primary driver for the growth of the neuromorphic hardware market.
#Neuromorphic Hardware Market Segment Landscape And Growth Potential
The neuromorphic hardware market covered in this report is segmented –
1) By Hardware Type: Digital Complementary Metal–Oxide–Semiconductor Or Neuromorphic, Analog Or Mixed-Signal Neuromorphic, Memristive Or Resistive, Photonic Neuromorphic, Spintronic Or Magnetic Tunnel Junction-Based
2) By Component: Processors Or Chips, Sensors, Memory, Software Development Kits And Development Tools, Reference Designs And Eval Kits
3) By Deployment: On-Premises, Cloud-Based, Hybrid, Data-Center Accelerators
4) By Application: Image Recognition, Signal Processing, Data Classification, Object Detection, Robotics Control, Edge Artificial Intelligence Processing
5) By End-Use Industry: Information Technology And Telecom, Automotive And Transportation, Healthcare And Life Sciences, Consumer Electronics, Aerospace And Defense, Industrial And Manufacturing, Others End-Use Industries
Subsegments:
1) By Digital Complementary Metal–Oxide–Semiconductor Neuromorphic: Complementary Metal–Oxide–Semiconductor Based Processors, Complementary Metal–Oxide–Semiconductor Based Memory Devices, Complementary Metal–Oxide–Semiconductor Based Neuromorphic Circuits, Complementary Metal–Oxide–Semiconductor Based Integrated Systems
2) By Analog Or Mixed-Signal Neuromorphic: Analog Synaptic Array Circuits, Mixed-Signal Neuromorphic Processing Units, Analog Neural Network Accelerators, Mixed-Signal Computational Cores
3) By Memristive Or Resistive: Memristor Based Synaptic Devices, Resistive Random Access Memory Arrays, Memristive Neural Network Accelerators, Resistive Switching Logic Elements
4) By Photonic Neuromorphic: Photonic Neural Network Processors, Optical Synaptic Interconnect Devices, Photonic Computing Modules, Integrated Photonic Neuromorphic Circuits
5) By Spintronic Or Magnetic Tunnel Junction-Based Hardware Types: Spintronic Synaptic Element Arrays, Magnetic Tunnel Junction Neural Cores, Spintronic Logic Processing Units, Spintronic Memory Accelerator Devices
Neuromorphic Hardware Market Transformation Trends: Which Innovations Are Driving Change?
Leading companies within the bridge access gantry systems market are concentrating on creating advanced solutions, particularly next-generation under-bridge inspection platforms, to boost structural accessibility, increase inspection precision, and elevate operational safety during infrastructure maintenance. These next-generation under-bridge inspection platforms are mobile systems that extend both horizontally and vertically beneath bridge decks, offering stable, controlled, and adaptable positioning for tasks like inspection and minor repairs. For example, in June 2025, Moog, a Germany-based producer of access and lifting equipment, introduced the MBI 110 NG, its newly engineered under-bridge inspection platform providing greater reach and enhanced maneuverability. This new generation bridge access gantry system incorporates a moving counterweight to guarantee ideal weight distribution in all working scenarios, significantly improving stability and safety. It also features an inclination compensation system, allowing for steady operation on slopes up to 6% longitudinally or 12% laterally without requiring operator input. Furthermore, a drive unit for creep mode is directly integrated into its solid rubber support wheels for ultimate power transmission, thereby improving control and efficiency for inspection personnel.
Neuromorphic Hardware Market Competitive Landscape: Who Are The Leading Companies?
Major companies operating in the neuromorphic hardware market are Samsung Electronics Co. Ltd., International Business Machines Corporation, Intel Corporation, Mythic Inc., Prophesee S.A., SynSense AG, Innatera Nanosystems B.V., Crocus Technology Inc., Syntiant Corp., Aspinity Inc., Crossbar Inc., GrAI Matter Labs (GML), BrainChip Holdings Ltd., Weebit Nano Ltd., iniVation AG, Knowm Inc., Neurophos Ltd., Eta Compute Inc., Applied Brain Research Inc., MemryX Inc.
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Neuromorphic Hardware Market Regional Outlook: Where Are The Largest Opportunities Located?
North America was the largest region in the neuromorphic hardware market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the neuromorphic hardware market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.