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Neuromorphic Computing Market
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Neuromorphic Computing Market

Author: Ranjana Pant - Research Analyst, Report ID - DS1101009, Published - December 2024

Segmented in Technology Type (Analog, Digital), Application (Image Recognition, Signal Recognition, Data Mining), Offering, Industry Vertical and Regions - Global Industry Analysis, Size, Share, Trends, and Forecast 2024 – 2034

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Global Neuromorphic Computing Market Outlook

Neuromorphic Computing stands at the forefront of the tech revolution with a potential to revolutionize different industries profoundly. The market, for Neuromorphic computing was estimated at $3.6 billion in 2024. It is anticipated to increase to $13.3 billion by 2030 with projections indicating a growth to around $39.7 billion by 2035. This expansion represents a compound annual growth rate (CAGR) of 24.5% over the forecast period. Its use and transformative abilities make it a game changer that could lead to major shifts not only in computing but also, in artificial intelligence and robotics. By enabling the development of machines that mimic thinking and learning processes this groundbreaking technology is expected to usher in a new era of progress driving remarkable advancements and transformations across various applications worldwide.


Neuromorphic Computings special power comes from its ability to imitate the neural structure and functions of the human brain to handle complex calculations effectively.


Market Size Forecast & Key Insights

2019
$3.6B2024
2029
$31.9B2034

Absolute Growth Opportunity = $28.3B

The Neuromorphic Computing market is projected to grow from $3.6 billion in 2024 to $31.9 billion in 2034. This represents a CAGR of 24.5%, reflecting rising demand across Machine Vision, Real-Time Data Analysis and Autonomous Robots.

The Neuromorphic Computing market is set to add $28.3 billion between 2024 and 2034, with industry players targeting Signal Recognition & Data Mining Application projected to gain a larger market share.

With Advancement in computing technology, and Dominance of ai and machine learning applications, Neuromorphic Computing market to expand 795% between 2024 and 2034.

Opportunities in the Neuromorphic Computing Market

Energy-efficient Data Centers

The increasing demand for energy data solutions has led to the rise of neuromorphic computing as a promising solution to this evolving issue. Its design inspired by the brain allows for power consumption and aligns with the current focus, on developing greener and more sustainable data centers.

AI-driven Healthcare Applications and Enhanced IoT Devices

Neuromorphic computing aims to replicate the processing power of the human brain and holds great promise in the realm of healthcare applications that remain largely unexplored. With its capacity to handle volumes of data rapidly and effectively in tow harnessing AI driven healthcare has the potential to enhance accessibility and precision making predictive diagnoses and advanced patient monitoring a reality.

Given the growth of the Internet of Things (IoT) neuromorphic computing has the potential to bring about significant changes in this field. With the increasing need for efficient IoT devices that can respond quickly to inputs and data processing requirements comes an opening for neuromorphic computing to shine with its real time processing capabilities. The incorporation of chips, into IoT devices could usher in a new era characterized by highly responsive and intelligent devices operating autonomously and thereby reshaping the competitive landscape of the market.

Growth Opportunities in North America and Europe

Europe Outlook

In Europe's Neuromorphic Computing sector is experiencing growth due to various factors like government emphasis on innovative technologies and ample funding for research projects along with a robust industrial presence in nations like Germany and the UK which all contribute to this upward trend in the market segment. Interestingly the rising adoption in sectors such as automotive and healthcare indicates promising prospects, for advancement and expansion.

North America Outlook

North America stands out as a hub for tech companies and is at the forefront of the Neuromorphic Computing market due to significant investments in research and development (R&D) emphasis, on artificial intelligence (AI) and machine learning (ML) technologies and a thriving startup community that drives innovation. Although facing competition the North American market offers considerable potential especially within the technology sector and defense industry.

North America Outlook

North America stands out as a hub for tech companies and is at the forefront of the Neuromorphic Computing market due to significant investments in research and development (R&D) emphasis, on artificial intelligence (AI) and machine learning (ML) technologies and a thriving startup community that drives innovation. Although facing competition the North American market offers considerable potential especially within the technology sector and defense industry.

Europe Outlook

In Europe's Neuromorphic Computing sector is experiencing growth due to various factors like government emphasis on innovative technologies and ample funding for research projects along with a robust industrial presence in nations like Germany and the UK which all contribute to this upward trend in the market segment. Interestingly the rising adoption in sectors such as automotive and healthcare indicates promising prospects, for advancement and expansion.

Growth Opportunities in North America and Europe

Established and Emerging Market's Growth Trend 2025–2034

1

Major Markets : United States, China, Germany, United Kingdom, Japan are expected to grow at 23.5% to 34.3% CAGR

2

Emerging Markets : Brazil, India, South Africa are expected to grow at 18.4% to 25.5% CAGR

Market Analysis Chart

Neuromorphic computing is a rising field that seeks to replicate the functions of the brain and finds applications in sectors such as robotics and healthcare among others The growth of this field is fueled by the rising volume of data and the demand for fast computing speeds Also driving this trend is the increasing focus on brain inspired computing, for deep learning tasks

Recent Developments and Technological Advancement

December 2024

Intel has revealed its cutting edge neuromorphic computing chip called Loih 2. This new chip boasts an efficiency that is ten times greater, than its predecessor.

November 2024

IBM has effectively combined its neuromorphic computing technology with cloud storage platforms to significantly enhance data processing speeds.

September 2024

Hewlett Packard Enterprises (known as HPE in the tech world) entered the field of computing by introducing a cutting edge prototype that is highly energy efficient.

Neuromorphic Computing is an evolving area of computer science that focuses on image and pattern recognition and is experiencing notable advancements at present. These technologies imitate the architecture of the human brain to execute intricate calculations, with greater speed and efficiency.

Impact of Industry Transitions on the Neuromorphic Computing Market

As a core segment of the IT industry, the Neuromorphic Computing market develops in line with broader industry shifts. Over recent years, transitions such as Artificial Intelligence Ascendancy and Shift Towards Edge Computing have redefined priorities across the IT sector, influencing how the Neuromorphic Computing market evolves in terms of demand, applications and competitive dynamics. These transitions highlight the structural changes shaping long-term growth opportunities.

1

Artificial Intelligence Ascendancy:

The rapid growth in the Neuromorphic Computing industry is driven by the adoption of artificial intelligence (AI). These systems that emulate the structure of the human brain are playing a crucial role in advancing AI technologies. The rising need for processing speeds and more efficient data management with lower energy consumption is fueling the integration of AI, into neuromorphic computing systems.

2

Shift Towards Edge Computing:

During this journey of transformation in the field of computing industry stands out the significant move towards edge computing as a pivotal transition. Edge computing boosts the effectiveness of chips by handling data closer, to its origin point.

Global Events Shaping Future Growth

The chart below highlights how external events including emerging market developments, regulatory changes, and technological disruptions, have added another layer of complexity to the IT industry. These events have disrupted supply networks, changed consumption behavior, and reshaped growth patterns. Together with structural industry transitions, they demonstrate how changes within the IT industry cascade into the Neuromorphic Computing market, setting the stage for its future growth trajectory.

Market Dynamics and Supply Chain

Driver: Advancement in Computing Technology, and Scalability and Energy Efficiency

The rise in the need for computing power to handle various intricate tasks has also played a significant role in the expansion of the neuromorphic computing sectors growth trajectory. Chips ability to emulate human brain functions has also made them well suited for tasks like big data analysis and pattern recognition. This has also led to an increase in their popularity and demand for applications such, as analytics and other sophisticated computing activities.
The demand, for increased scalability and energy saving computing solutions to align with sustainability goals and improve efficiency is also propelling the growth of the neuromorphic computing sector.
The growth of AI and machine learning connected to the progress in computing technology is also a factor pushing forward the neuromorphic computing markets development. Its importance lies in its capability to learn and evolve with time which makes it suitable, for complex artificial intelligence tasks.

Restraint: High Implementation Cost

A major hurdle in the realm of computing is the steep implementation costs involved in creating neuromorphic chips necessitating a considerable investment, in research and development funds which could hinder market expansion as many interested parties might find it hard to finance such substantial ventures This obstacle is especially daunting in underdeveloped markets where resources are limited and the technological framework may not be sufficiently advanced to accommodate neuromorphic computing.

Challenge: Limited Technological Understanding

The growth of computing faces a significant obstacle due to the lack of widespread knowledge and awareness about this technology, among non experts despite its promising advantages and intricate nature.

Supply Chain Landscape

Research & Development

Intel

HP Labs

Component Manufacturing

IBM

Qualcomm

Toshiba

System Integration
BrainChip Inc / HRL Laboratories
End Users
Aerospace & Defense Industries / Healthcare Industries / Consumer Electronics Industries
Research & Development

Intel

HP Labs

Component Manufacturing

IBM

Qualcomm

Toshiba

System Integration

BrainChip Inc

HRL Laboratories

End Users

Aerospace & Defense Industries

Healthcare Industries

Consumer Electronics Industries

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Leading Providers and Their Strategies

Application AreaIndustryLeading ProvidersProvider Strategies
Neuromorphic Sensing and Processing
Consumer Electronics, Automotive, Industrial, Healthcare
IBM, Intel
Investment in research and development, Collaboration with academic institutions, Emphasizing on chip efficiency and low power consumption
Neuromorphic Vision and Audio Recognition
Security, Surveillance, Automotive, Retail
IBM, Qualcomm
Development of advanced solutions, Strategic partnerships with leading tech companies, Offering highly efficient and scalable solutions
Cognitive and Swarm Robotics
Manufacturing, Automotive, Logistics, Healthcare
HP, General Vision
Leveraging advanced computing capabilities, Partnership with industrial robotics manufacturers, Aiming for automation and process efficiency improvement
AI and Deep Learning Platforms
Technology, Finance, Healthcare, Retail
Google, IBM, Intel
Focus on AI advancement, Innovation in learning algorithms development, Enhancing machine learning capabilities through neuromorphic computing solutions

Elevate your strategic vision with in-depth analysis of key applications, leading market players, and their strategies. The report analyzes industry leaders' views and statements on the Neuromorphic Computing market's present and future growth.

Our research is created following strict editorial standards. See our Editorial Policy

Applications of Neuromorphic Computing in Real-Time Data Analysis, Machine Vision and Autonomous Robots

Real-Time Data Analysis

Neuromorphic computing is widely used for real time data analysis due to its ability to quickly and effectively process datasets and provide immediate insights essential for time sensitive situations like high frequency trading activities. The TrueNorth neuromorphic chip from IBM is a player, in this field of technology.

Machine Vision

Neuromorphic computing is widely used in machine vision to help machines interpret and comprehend the environment they encounter in real time. This technology allows for fast image analysis which aids in making decisions accurately. Intel is a player in this field, with its research chip called Loihi that leverages neuromorphic capabilities.

Autonomous Robots

Neuromorphic computing plays a role in empowering autonomous robots to learn and adjust to their surroundings instantly—an ability reminiscent of the human brains functioning style. This advancement offers robots autonomy and performance levels in intricate environments while also lowering power consumption and computing demands. Qualcomm stands out as a player, in this field by enhancing robotics through their Snapdragon neuromorphic processor.

Neuromorphic Computing vs. Substitutes:
Performance and Positioning Analysis

Neuromorphic Computing imitates the functions of the brain and offers a more effective and adaptable method compared to conventional computing or Machine Learning methods. It holds a market position with significant growth prospects attributable, to its cognitive computing abilities.

Neuromorphic Computing
  • Quantum Computing /
  • Deep Learning Networks /
  • Traditional Von Neumann Architecture
    High processing speed, Efficient energy utilization
    High implementation cost, Limited understanding and knowledge in the field
    High processing speed, efficiency in energy usage
    Limited scalability, lack of flexibility in deployment

Neuromorphic Computing vs. Substitutes:
Performance and Positioning Analysis

Neuromorphic Computing

  • High processing speed, Efficient energy utilization
  • High implementation cost, Limited understanding and knowledge in the field

Quantum Computing / Deep Learning Networks / Traditional Von Neumann Architecture

  • High processing speed, efficiency in energy usage
  • Limited scalability, lack of flexibility in deployment

Neuromorphic Computing imitates the functions of the brain and offers a more effective and adaptable method compared to conventional computing or Machine Learning methods. It holds a market position with significant growth prospects attributable, to its cognitive computing abilities.

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Research Methodology

This market research methodology defines the Neuromorphic Computing market scope, gathers reliable data, and validates findings through integrated primary and secondary research. Our systematic framework ensures precise market sizing, adoption analysis, and competitive benchmarking tailored to solution-driven ecosystems.


Secondary Research Approach


We initiate secondary research by defining the targeted market at both Regional and Country levels. As part of the IT ecosystem, we analyze Neuromorphic Computing across Image Recognition, Signal Recognition, and Data Mining Applications. Data is systematically gathered from country level ministerial sources, industry associations & federations, company annual & quarterly reports and other credential sources, allowing us to map solution deployment trends, pricing models, compliance requirements, and technology adoption pathways.


Key Sources Referenced:

• Annual Business Surveys (US, EU, Japan)

• NAICS - Economic Statistics (US, Canada) / IMF DSBB

Annual Reports / Industry Magazines / Country Level

DataString Database

We benchmark competitors such as IBM, Intel Corporation, and Qualcomm Incorporated using verified industry reports, customer case studies, company disclosures, and partner ecosystem strategies. Our secondary insights uncover solution-specific drivers and inhibitors, which form the foundation for targeted primary research.


Primary Research Methods


We conduct structured interviews and surveys with solution stakeholders, including Research & Development, Component Manufacturing, and System Integration. Geographic coverage spans North America (45%), Europe (33%), and Asia-Pacific (22%) and Middle East & Africa (5%). Our online surveys generally achieve a 72% response rate, while expert interviews deliver an 86% engagement level, resulting in a 93% confidence level with ±6.1% margin of error.


Through targeted questionnaires and in-depth interviews, we capture adoption motivators, integration challenges, return-on-investment perceptions, and solution stickiness across enterprise segments. These primary insights validate secondary findings and align market sizing with real-world conditions.


Market Engineering and Data Analysis Framework


Our data analysis framework integrates Top-Down, Bottom-Up, and Adoption-Rate modeling to forecast solution demand with precision.


Top-down and Bottom-up Process


In the Top-down approach, we disaggregate global IT revenues to estimate the Neuromorphic Computing segment, leveraging enterprise digitalization budgets and IT spending patterns. In the Bottom-up approach, we aggregate deployment data at the country and vertical levels, considering subscription volumes, integration projects, and solution renewals to forecast regional and global adoption. By reconciling both approaches, we ensure statistical robustness and forecast reliability.


We further map the solution delivery value chain spanning Research & Development (Intel, HP Labs), Component Manufacturing (IBM, Qualcomm), and System Integration. Our parallel substitute analysis examines Quantum Computing, Deep Learning Networks, and Traditional Von Neumann Architecture, highlighting diversification opportunities and competitive risks.


Company Market Share and Benchmarking


We benchmark leading solution providers such as IBM, Intel Corporation, and Qualcomm Incorporated, analyzing their strengths in deployment scalability, integration capabilities, customer retention, and partner ecosystem development. Company revenues, case deployments, and recurring revenue streams are assessed to estimate market shares and clarify competitive positioning.


Our integration of triangulated data, ecosystem mapping, and solution benchmarking, enhanced by our proprietary Directional Superposition methodology, ensures precise forecasts and actionable strategic insights into the Neuromorphic Computing market.


Quality Assurance and Compliance


We cross-reference secondary data with primary inputs and external expert reviews to confirm consistency. Further, we use stratified sampling, anonymous surveys, third-party interviews, and time-based sampling to reduce bias and strengthen our results.


Our methodology is developed in alignment with ISO 20252 standards and ICC/ESOMAR guidelines for research ethics. The study methodology follows globally recognized frameworks such as ISO 20252 and ICC codes of practice.

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Neuromorphic Computing Market Data: Size, Segmentation & Growth Forecast

Report AttributeDetails
Market Value in 2025USD 4.4 billion
Revenue Forecast in 2034USD 31.9 billion
Growth RateCAGR of 24.5% from 2025 to 2034
Base Year for Estimation2024
Industry Revenue 20243.6 billion
Growth OpportunityUSD 28.3 billion
Historical Data2019 - 2023
Growth Projection / Forecast Period2025 - 2034
Market Size UnitsMarket Revenue in USD billion and Industry Statistics
Market Size 20243.6 billion USD
Market Size 20276.9 billion USD
Market Size 202910.6 billion USD
Market Size 203013.3 billion USD
Market Size 203431.9 billion USD
Market Size 203539.7 billion USD
Report CoverageMarket revenue for past 5 years and forecast for future 10 years, Competitive Analysis & Company Market Share, Strategic Insights & trends
Segments CoveredTechnology Type, Application, Offering, Industry Vertical
Regional scopeNorth America, Europe, Asia Pacific, Latin America and Middle East & Africa
Country scopeU.S., Canada, Mexico, UK, Germany, France, Italy, Spain, China, India, Japan, South Korea, Brazil, Mexico, Argentina, Saudi Arabia, UAE and South Africa
Companies ProfiledIBM, Intel Corporation, Qualcomm Incorporated, Hewlett Packard Enterprise, General Vision Inc, BrainChip Holdings Ltd, Vicarious, HRL Laboratories LLC, Knowm Inc, Applied Brain Research Inc, Numenta and Recursion Pharmaceuticals Inc
CustomizationFree customization at segment, region or country scope and direct contact with report analyst team for 10 to 20 working hours for any additional niche requirement which is almost equivalent to 10% of report value

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Table of Contents

Industry Insights Report - Table Of Contents

Chapter 1

Executive Summary

Major Markets & Their Performance - Statistical Snapshots

Chapter 2

Research Methodology

2.1Axioms & Postulates
2.2Market Introduction & Research MethodologyEstimation & Forecast Parameters / Major Databases & Sources
Chapter 3

Market Dynamics

3.1Market OverviewDrivers / Restraints / Opportunities / M4 Factors
3.2Market Trends
3.2.1Introduction & Narratives
3.2.2Market Trends - Impact Analysis(Short, Medium & Long Term Impacts)
3.3Supply Chain Analysis
3.4Porter's Five ForcesSuppliers & Buyers' Bargaining Power, Threat of Substitution & New Market Entrants, Competitive Rivalry
Chapter 4

Neuromorphic Computing Market Size, Opportunities & Strategic Insights, by Technology Type

4.1Analog
4.2Digital
Chapter 5

Neuromorphic Computing Market Size, Opportunities & Strategic Insights, by Application

5.1Image Recognition
5.2Signal Recognition
5.3Data Mining
Chapter 6

Neuromorphic Computing Market Size, Opportunities & Strategic Insights, by Offering

6.1Hardware
6.2Software
Chapter 7

Neuromorphic Computing Market Size, Opportunities & Strategic Insights, by Industry Vertical

7.1Healthcare
7.2Automotive
7.3Consumer Electronics
Chapter 8

Neuromorphic Computing Market, by Region

8.1North America Neuromorphic Computing Market Size, Opportunities, Key Trends & Strategic Insights
8.1.1U.S.
8.1.2Canada
8.2Europe Neuromorphic Computing Market Size, Opportunities, Key Trends & Strategic Insights
8.2.1Germany
8.2.2France
8.2.3UK
8.2.4Italy
8.2.5The Netherlands
8.2.6Rest of EU
8.3Asia Pacific Neuromorphic Computing Market Size, Opportunities, Key Trends & Strategic Insights
8.3.1China
8.3.2Japan
8.3.3South Korea
8.3.4India
8.3.5Australia
8.3.6Thailand
8.3.7Rest of APAC
8.4Middle East & Africa Neuromorphic Computing Market Size, Opportunities, Key Trends & Strategic Insights
8.4.1Saudi Arabia
8.4.2United Arab Emirates
8.4.3South Africa
8.4.4Rest of MEA
8.5Latin America Neuromorphic Computing Market Size, Opportunities, Key Trends & Strategic Insights
8.5.1Brazil
8.5.2Mexico
8.5.3Rest of LA
8.6CIS Neuromorphic Computing Market Size, Opportunities, Key Trends & Strategic Insights
8.6.1Russia
8.6.2Rest of CIS
Chapter 9

Competitive Landscape

9.1Competitive Dashboard & Market Share Analysis
9.2Company Profiles (Overview, Financials, Developments, SWOT)
9.2.1IBM
9.2.2Intel Corporation
9.2.3Qualcomm Incorporated
9.2.4Hewlett Packard Enterprise
9.2.5General Vision Inc
9.2.6BrainChip Holdings Ltd
9.2.7Vicarious
9.2.8HRL Laboratories LLC
9.2.9Knowm Inc
9.2.10Applied Brain Research Inc
9.2.11Numenta
9.2.12Recursion Pharmaceuticals Inc