Summary

Neuromorphic Computing Market Overview
As per Roots Analysis, the global neuromorphic computing market size is estimated to grow from USD 2.60 billion in the current year to USD 61.48 billion by 2035, at a CAGR of 33.32% during the forecast period, till 2035.

https://www.rootsanalysis.com/img100/neuromorphic-computing-market-slide-1.png

The opportunity for neuromorphic computing market has been distributed across the following segments:

Type of Offering
• Hardware
• Memory
• Processors
• Sensors
• Others
• Software
• Platform for Neuromorphic Development
• Simulation and Modeling Tools

Type of Application
• Data Processing
• Image Processing
• Object Processing
• Pattern Recognition
• Signal Processing
• Others

Type of Deployment
• Cloud Computing
• Edge Computing

Type of End User
• Automotive
• Consumer Electronics
• Healthcare
• Industrial
• IT & Telecom
• Military & Defense
• Retail
• Others

Geographical Regions
• North America
• US
• Canada
• Mexico
• Other North American countries
• Europe
• Austria
• Belgium
• Denmark
• France
• Germany
• Ireland
• Italy
• Netherlands
• Norway
• Russia
• Spain
• Sweden
• Switzerland
• UK
• Other European countries
• Asia
• China
• India
• Japan
• Singapore
• South Korea
• Other Asian countries
• Latin America
• Brazil
• Chile
• Colombia
• Venezuela
• Other Latin American countries
• Middle East and North Africa
• Egypt
• Iran
• Iraq
• Israel
• Kuwait
• Saudi Arabia
• UAE
• Other MENA countries
• Rest of the World
• Australia
• New Zealand
• Other countries

Neuromorphic Computing Market: Growth and Trends
Neuromorphic computing is a computing paradigm that mimics the functioning of the human brain. It typically involves both hardware and software designed to emulate the brain’s neural structure and synapses, allowing for more natural and efficient information processing. The first silicon neurons and synapses were created by Misha Mahowald and Carver Mead, who established the neuromorphic computing model in 1980. This approach is based on the biological method where the brain processes information in parallel through a network of interconnected neurons and synapses, which transmit chemical and electrical signals to facilitate communication between neurons.

In this regard, spiking neural networks (SNNs) represent a fundamental concept of neuromorphic computing, reflecting how biological systems communicate. SNNs consist of artificial neurons and synapses that spike, differing from traditional artificial neural networks (ANNs) that rely on continuous synchronous signals; instead, SNNs use spikes for data processing, improving power efficiency in real-time edge applications.

Within this framework, the hardware for neuromorphic computing includes specialized chips designed to replicate brain-like processing, playing a crucial role. These neuromorphic chips function based on neuromorphic principles to execute various artificial intelligence tasks, such as recognition, learning, and decision-making, more effectively than conventional silicon-based architectures. This advanced computing technology has enabled industries to develop machines capable of performing complex tasks with greater efficiency and precision.

The aim of neuromorphic systems is to function with significantly reduced power consumption, excelling in low-power applications such as mobile devices, edge computing solutions, and sensor networks. Furthermore, their ability to process data in parallel, handle real-time information, and adaptively learn with scalability underscores their significance across diverse sectors, including AI, robotics, healthcare, and energy-efficient computing. As the demand for artificial intelligence and machine learning rises, along with the integration of neuromorphic systems in healthcare, the neuromorphic computing market is expected to experience significant growth during the forecast period.

Neuromorphic Computing Market: Key Segments

Market Share by Type of Offering
Based on type of offering, the global neuromorphic computing market is segmented into hardware and software. According to our estimates, currently, the hardware segment which consists of neuromorphic processors, memory chips, sensors, and other devices, captures the majority share of the market. This can be attributed to the extensive development of neuromorphic chips, essential for brain-inspired computing architectures, which are crucial for executing tasks like real-time data processing, decision-making, and pattern recognition, thereby propelling market growth.

However, the market for software segment is expected to grow at a higher CAGR during the forecast period, driven by the growing adoption of neuromorphic computing software across various sectors for simulation and algorithm development, particularly with cloud deployment options available.

Market Share by Type of Application
Based on type of application, the neuromorphic computing market is segmented into data processing, image processing, object processing, pattern recognition, signal processing, and others. According to our estimates, currently, the image-processing application captures the majority of the market. This can be attributed to the substantial demand from autonomous vehicles where image processing is crucial for tasks like object detection, lane tracking, and real-time decision-making. Further, the extensive utilization of image processing in medical imaging, robotics, drones, and consumer electronics boosts the demand for neuromorphic computing.

However, the signal processing segment is expected to grow at a higher CAGR during the forecast period. This can be ascribed to the increasing demand from telecommunications aimed at optimizing network traffic management, signal transmission, and data routing. Additionally, the growing adoption of this technology in hearing aids, radar, and sonar systems is also expected to contribute to market growth.

Market Share by Type of Deployment
Based on type of deployment, the neuromorphic computing market is segmented into edge computing and cloud computing deployment. According to our estimates, currently, edge computing deployment captures the majority share of the market. This can be attributed to the critical role of edge computing in achieving low latency and real-time processing, enabling devices to react immediately without delays in data transmission. Additionally, edge devices typically operate with limited power resources, making them energy-efficient, which aligns well with neuromorphic chips designed for local data processing.

However, the cloud computing segment is expected to grow at a higher CAGR during the forecast period. This can be ascribed to the continuous technological advancements in a comprehensive platform for managing large volumes of data for businesses.

Market Share by Type of End User
Based on type of end user, the neuromorphic computing market is segmented into automotive, consumer electronics, healthcare, industrial, IT& telecom, military & defense, retail, and others. According to our estimates, currently, military and defense sector captures the majority share of the market. This can be attributed to the sector's specific needs and its uses in areas such as radar systems, surveillance, and combat systems, which require real-time decision-making, sophisticated data processing, and energy efficiency, thereby driving the growth of the neuromorphic computing market.

However, the automotive sector is expected to grow at a higher CAGR during the forecast period, owing to the increasing production of autonomous vehicles and advanced driver-assistance systems.

Market Share by Geographical Regions
Based on geographical regions, the neuromorphic computing market is segmented into North America, Europe, Asia, Latin America, Middle East and North Africa, and the rest of the world. According to our estimates, currently, North America captures the majority share of the market. However, the market in Asia is expected to grow at a higher CAGR during the forecast period, owing to the increased adoption of artificial intelligence, machine learning, IoT, and deep learning technologies, along with the growth of the IT sector in the region.

Example Players in Neuromorphic Computing Market
• Accenture
• Brain Chip Holdings
• Cadence-Design
• CEA-Leti
• General Vision
• Gr AI Matter Labs
• Hewlett Packard
• HP
• HRL Laboratories
• IBM
• Innatera Nanosytems
• Instar Robotics
• Intel
• Known
• Koniku
• Numenta
• Qualcomm
• Samsung Electronics
• SK HynixNVIDIA
• SynsSense
• Vicarious

Neuromorphic Computing Market: Research Coverage
The report on the neuromorphic computing market features insights on various sections, including:
• Market Sizing and Opportunity Analysis: An in-depth analysis of the neuromorphic computing market, focusing on key market segments, including [A] type of offering, [B] type of application, [C] type of deployment, [D] type of end user, and [E] geographical regions.
• Competitive Landscape: A comprehensive analysis of the companies engaged in the neuromorphic computing market, based on several relevant parameters, such as [A] year of establishment, [B] company size, [C] location of headquarters and [D] ownership structure.
• Company Profiles: Elaborate profiles of prominent players engaged in the neuromorphic computing market, providing details on [A]  location of headquarters, [B] company size, [C] company mission, [D] company footprint, [E] management team, [F] contact details, [G] financial information, [H] operating business segments, [I] neuromorphic computing portfolio, [J] moat analysis, [K] recent developments, and an informed future outlook.
• Megatrends: An evaluation of ongoing megatrends in neuromorphic computing industry.
• Patent Analysis: An insightful analysis of patents filed / granted in the neuromorphic computing domain, based on relevant parameters, including [A] type of patent, [B] patent publication year, [C] patent age and [D] leading players.
• Recent Developments: An overview of the recent developments made in the neuromorphic computing market, along with analysis based on relevant parameters, including [A] year of initiative, [B] type of initiative, [C] geographical distribution and [D] most active players.
• Porter’s Five Forces Analysis: An analysis of five competitive forces prevailing in the neuromorphic computing market, including threats of new entrants, bargaining power of buyers, bargaining power of suppliers, threats of substitute products and rivalry among existing competitors.
• SWOT Analysis: An insightful SWOT framework, highlighting the strengths, weaknesses, opportunities and threats in the domain. Additionally, it provides Harvey ball analysis, highlighting the relative impact of each SWOT parameter.
• Value Chain Analysis: A comprehensive analysis of the value chain, providing information on the different phases and stakeholders involved in the neuromorphic computing market.

Key Questions Answered in this Report
• How many companies are currently engaged in neuromorphic computing market?
• Which are the leading companies in this market?
• What factors are likely to influence the evolution of this market?
• What is the current and future market size?
• What is the CAGR of this market?
• How is the current and future market opportunity likely to be distributed across key market segments?

Reasons to Buy this Report
• The report provides a comprehensive market analysis, offering detailed revenue projections of the overall market and its specific sub-segments. This information is valuable to both established market leaders and emerging entrants.
• Stakeholders can leverage the report to gain a deeper understanding of the competitive dynamics within the market. By analyzing the competitive landscape, businesses can make informed decisions to optimize their market positioning and develop effective go-to-market strategies.
• The report offers stakeholders a comprehensive overview of the market, including key drivers, barriers, opportunities, and challenges. This information empowers stakeholders to stay abreast of market trends and make data-driven decisions to capitalize on growth prospects.

Additional Benefits
• Complimentary Excel Data Packs for all Analytical Modules in the Report
• 15% Free Content Customization
• Detailed Report Walkthrough Session with Research Team
• Free Updated report if the report is 6-12 months old or older

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Table of Contents

SECTION I: REPORT OVERVIEW

1. PREFACE
1.1. Introduction
1.2. Market Share Insights
1.3. Key Market Insights
1.4. Report Coverage
1.5. Key Questions Answered
1.6. Chapter Outlines

2. RESEARCH METHODOLOGY
2.1. Chapter Overview
2.2. Research Assumptions
2.3. Database Building
2.3.1. Data Collection
2.3.2. Data Validation
2.3.3. Data Analysis

2.4. Project Methodology
2.4.1. Secondary Research
2.4.1.1. Annual Reports
2.4.1.2. Academic Research Papers
2.4.1.3. Company Websites
2.4.1.4. Investor Presentations
2.4.1.5. Regulatory Filings
2.4.1.6. White Papers
2.4.1.7. Industry Publications
2.4.1.8. Conferences and Seminars
2.4.1.9. Government Portals
2.4.1.10. Media and Press Releases
2.4.1.11. Newsletters
2.4.1.12. Industry Databases
2.4.1.13. Roots Proprietary Databases
2.4.1.14. Paid Databases and Sources
2.4.1.15. Social Media Portals
2.4.1.16. Other Secondary Sources
2.4.2. Primary Research
2.4.2.1. Introduction
2.4.2.2. Types
2.4.2.2.1. Qualitative
2.4.2.2.2. Quantitative
2.4.2.3. Advantages
2.4.2.4. Techniques
2.4.2.4.1. Interviews
2.4.2.4.2. Surveys
2.4.2.4.3. Focus Groups
2.4.2.4.4. Observational Research
2.4.2.4.5. Social Media Interactions
2.4.2.5. Stakeholders
2.4.2.5.1. Company Executives (CXOs)
2.4.2.5.2. Board of Directors
2.4.2.5.3. Company Presidents and Vice Presidents
2.4.2.5.4. Key Opinion Leaders
2.4.2.5.5. Research and Development Heads
2.4.2.5.6. Technical Experts
2.4.2.5.7. Subject Matter Experts
2.4.2.5.8. Scientists
2.4.2.5.9. Doctors and Other Healthcare Providers
2.4.2.6. Ethics and Integrity
2.4.2.6.1. Research Ethics
2.4.2.6.2. Data Integrity

2.4.3. Analytical Tools and Databases

3. MARKET DYNAMICS
3.1. Forecast Methodology
3.1.1. Top-Down Approach
3.1.2. Bottom-Up Approach
3.1.3. Hybrid Approach
3.2. Market Assessment Framework
3.2.1. Total Addressable Market (TAM)
3.2.2. Serviceable Addressable Market (SAM)
3.2.3. Serviceable Obtainable Market (SOM)
3.2.4. Currently Acquired Market (CAM)
3.3. Forecasting Tools and Techniques
3.3.1. Qualitative Forecasting
3.3.2. Correlation
3.3.3. Regression
3.3.4. Time Series Analysis
3.3.5. Extrapolation
3.3.6. Convergence
3.3.7. Forecast Error Analysis
3.3.8. Data Visualization
3.3.9. Scenario Planning
3.3.10. Sensitivity Analysis
3.4. Key Considerations
3.4.1. Demographics
3.4.2. Market Access
3.4.3. Reimbursement Scenarios
3.4.4. Industry Consolidation
3.5. Robust Quality Control
3.6. Key Market Segmentations
3.7. Limitations

4. MACRO-ECONOMIC INDICATORS
4.1. Chapter Overview
4.2. Market Dynamics
4.2.1. Time Period
4.2.1.1. Historical Trends
4.2.1.2. Current and Forecasted Estimates
4.2.2. Currency Coverage
4.2.2.1. Overview of Major Currencies Affecting the Market
4.2.2.2. Impact of Currency Fluctuations on the Industry
4.2.3. Foreign Exchange Impact
4.2.3.1. Evaluation of Foreign Exchange Rates and Their Impact on Market
4.2.3.2. Strategies for Mitigating Foreign Exchange Risk
4.2.4. Recession
4.2.4.1. Historical Analysis of Past Recessions and Lessons Learnt
4.2.4.2. Assessment of Current Economic Conditions and Potential Impact on the Market
4.2.5. Inflation
4.2.5.1. Measurement and Analysis of Inflationary Pressures in the Economy
4.2.5.2. Potential Impact of Inflation on the Market Evolution
4.2.6. Interest Rates
4.2.6.1. Overview of Interest Rates and Their Impact on the Market
4.2.6.2. Strategies for Managing Interest Rate Risk
4.2.7. Commodity Flow Analysis
4.2.7.1. Type of Commodity
4.2.7.2. Origins and Destinations
4.2.7.3. Values and Weights
4.2.7.4. Modes of Transportation
4.2.8. Global Trade Dynamics
4.2.8.1. Import Scenario
4.2.8.2. Export Scenario
4.2.9. War Impact Analysis
4.2.9.1. Russian-Ukraine War
4.2.9.2. Israel-Hamas War
4.2.10. COVID Impact / Related Factors
4.2.10.1. Global Economic Impact
4.2.10.2. Industry-specific Impact
4.2.10.3. Government Response and Stimulus Measures
4.2.10.4. Future Outlook and Adaptation Strategies
4.2.11. Other Indicators
4.2.11.1. Fiscal Policy
4.2.11.2. Consumer Spending
4.2.11.3. Gross Domestic Product (GDP)
4.2.11.4. Employment
4.2.11.5. Taxes
4.2.11.6. R&D Innovation
4.2.11.7. Stock Market Performance
4.2.11.8. Supply Chain
4.2.11.9. Cross-Border Dynamics

SECTION II: QUALITATIVE INSIGHTS

5. EXECUTIVE SUMMARY

6. INTRODUCTION
6.1. Chapter Overview
6.2. Overview of Neuromorphic Computing Market
6.2.1. Type of Offering
6.2.2. Type of Application
6.2.3. Type of Deployment
6.2.4. Type of End User

6.3. Future Perspective

7. REGULATORY SCENARIO

SECTION III: MARKET OVERVIEW

8. COMPREHENSIVE DATABASE OF LEADING PLAYERS

9. COMPETITIVE LANDSCAPE
9.1. Chapter Overview
9.2. Neuromorphic Computing: Overall Market Landscape
9.2.1. Analysis by Year of Establishment
9.2.2. Analysis by Company Size
9.2.3. Analysis by Location of Headquarters
9.2.4. Analysis by Ownership Structure

10. WHITE SPACE ANALYSIS

11. COMPANY COMPETITIVENESS ANALYSIS

12. STARTUP ECOSYSTEM IN THE NEUROMORPHIC COMPUTING MARKET
12.1. Neuromorphic Computing: Market Landscape of Startups
12.1.1. Analysis by Year of Establishment
12.1.2. Analysis by Company Size
12.1.3. Analysis by Company Size and Year of Establishment
12.1.4. Analysis by Location of Headquarters
12.1.5. Analysis by Company Size and Location of Headquarters
12.1.6. Analysis by Ownership Structure
12.2. Key Findings

SECTION IV: COMPANY PROFILES

13. COMPANY PROFILES
13.1. Chapter Overview
13.2. Accenture *
13.2.1. Company Overview
13.2.2. Company Mission
13.2.3. Company Footprint
13.2.4. Management Team
13.2.5. Contact Details
13.2.6. Financial Performance
13.2.7. Operating Business Segments
13.2.8. Service / Product Portfolio (project specific)
13.2.9. MOAT Analysis
13.2.10. Recent Developments and Future Outlook

* similar detail is presented for other below mentioned companies based on information in the public domain

13.3. BrainChip Holdings
13.4. Cadence Design Systems
13.5. CEA-Leti
13.6. General Vision
13.7. Gr AI Matter Labs
13.8. Hewlett Packard
13.9. HRL Laboratories
13.10. IBM
13.11. Innatera Nanosystems
13.12. Instar Robotics
13.13. Intel
13.14. Known
13.15. Koniku
13.16. Numenta
13.17. Qualcomm
13.18. Samsung Electronics
13.19. SK Hynix
13.20. NVIDIA
13.21. SynSense
13.22. Vicarious

SECTION V: MARKET TRENDS

14. MEGA TRENDS ANALYSIS
15. UNMEET NEED ANALYSIS
16. PATENT ANALYSIS
17. RECENT DEVELOPMENTS
17.1. Chapter Overview
17.2. Recent Funding
17.3. Recent Partnerships
17.4. Other Recent Initiatives

SECTION VI: MARKET OPPORTUNITY ANALYSIS

18. GLOBAL NEUROMORPHIC COMPUTING MARKET
18.1. Chapter Overview
18.2. Key Assumptions and Methodology
18.3. Trends Disruption Impacting Market
18.4. Demand Side Trends
18.5. Supply Side Trends
18.6. Global Neuromorphic Computing, Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
18.7. Multivariate Scenario Analysis
18.7.1. Conservative Scenario
18.7.2. Optimistic Scenario
18.8. Investment Feasibility Index
18.9. Key Market Segmentations

19. MARKET OPPORTUNITIES BASED ON TYPE OF OFFERING
19.1. Chapter Overview
19.2. Key Assumptions and Methodology
19.3. Revenue Shift Analysis
19.4. Market Movement Analysis
19.5. Penetration-Growth (P-G) Matrix
19.6. Neuromorphic Computing Market for Hardware: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
19.7. Neuromorphic Computing Market for Software: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
19.8. Data Triangulation and Validation
19.8.1. Secondary Sources
19.8.2. Primary Sources
19.8.3. Statistical Modeling

20. MARKET OPPORTUNITIES BASED ON TYPE OF APPLICATION
20.1. Chapter Overview
20.2. Key Assumptions and Methodology
20.3. Revenue Shift Analysis
20.4. Market Movement Analysis
20.5. Penetration-Growth (P-G) Matrix
20.6. Neuromorphic Computing Market for Data Processing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.7. Neuromorphic Computing Market for Image Processing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.8. Neuromorphic Computing Market for Object Processing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.9. Neuromorphic Computing Market for Pattern Recognition: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.10. Neuromorphic Computing Market for Signal Processing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.11. Neuromorphic Computing Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
20.12. Data Triangulation and Validation
20.12.1. Secondary Sources
20.12.2. Primary Sources
20.12.3. Statistical Modeling

21. MARKET OPPORTUNITIES BASED ON TYPE OF DEPLOYMENT
21.1. Chapter Overview
21.2. Key Assumptions and Methodology
21.3. Revenue Shift Analysis
21.4. Market Movement Analysis
21.5. Penetration-Growth (P-G) Matrix
21.6. Neuromorphic Computing Market for Cloud Computing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.7. Neuromorphic Computing Market for Edge Computing: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
21.8. Data Triangulation and Validation
21.8.1. Secondary Sources
21.8.2. Primary Sources
21.8.3. Statistical Modeling

22. MARKET OPPORTUNITIES BASED ON TYPE OF END USER
22.1. Chapter Overview
22.2. Key Assumptions and Methodology
22.3. Revenue Shift Analysis
22.4. Market Movement Analysis
22.5. Penetration-Growth (P-G) Matrix
22.6. Neuromorphic Computing Market for Automotive: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.7. Neuromorphic Computing Market for Consumer Electronics: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.8. Neuromorphic Computing Market for Healthcare: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.9. Neuromorphic Computing Market for Industrial: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.10. Neuromorphic Computing Market for IT & Telecom: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.11. Neuromorphic Computing Market for Military & Defense: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.12. Neuromorphic Computing Market for Retail: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.13. Neuromorphic Computing Market for Others: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
22.14. Data Triangulation and Validation
22.14.1. Secondary Sources
22.14.2. Primary Sources
22.14.3. Statistical Modeling

23. MARKET OPPORTUNITIES FOR NEUROMORPHIC COMPUTING IN NORTH AMERICA
23.1. Chapter Overview
23.2. Key Assumptions and Methodology
23.3. Revenue Shift Analysis
23.4. Market Movement Analysis
23.5. Penetration-Growth (P-G) Matrix
23.6. Neuromorphic Computing Market in North America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.6.1. Neuromorphic Computing Market in the US: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.6.2. Neuromorphic Computing Market in Canada: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.6.3. Neuromorphic Computing Market in Mexico: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.6.4. Neuromorphic Computing Market in Other North American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
23.7. Data Triangulation and Validation

24. MARKET OPPORTUNITIES FOR NEUROMORPHIC COMPUTING IN EUROPE
24.1. Chapter Overview
24.2. Key Assumptions and Methodology
24.3. Revenue Shift Analysis
24.4. Market Movement Analysis
24.5. Penetration-Growth (P-G) Matrix
24.6. Neuromorphic Computing Market in Europe: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.1. Neuromorphic Computing Market in Austria: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.2. Neuromorphic Computing Market in Belgium: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.3. Neuromorphic Computing Market in Denmark: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.4. Neuromorphic Computing Market in France: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.5. Neuromorphic Computing Market in Germany: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.6. Neuromorphic Computing Market in Ireland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.7. Neuromorphic Computing Market in Italy: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.8. Neuromorphic Computing Market in Netherlands: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.9. Neuromorphic Computing Market in Norway: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.10. Neuromorphic Computing Market in Russia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.11. Neuromorphic Computing Market in Spain: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.12. Neuromorphic Computing Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.13. Neuromorphic Computing Market in Sweden: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.14. Neuromorphic Computing Market in Switzerland: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.15. Neuromorphic Computing Market in the UK: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.6.16. Neuromorphic Computing Market in Other European Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
24.7. Data Triangulation and Validation

25. MARKET OPPORTUNITIES FOR NEUROMORPHIC COMPUTING IN ASIA
25.1. Chapter Overview
25.2. Key Assumptions and Methodology
25.3. Revenue Shift Analysis
25.4. Market Movement Analysis
25.5. Penetration-Growth (P-G) Matrix
25.6. Neuromorphic Computing Market in Asia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.1. Neuromorphic Computing Market in China: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.2. Neuromorphic Computing Market in India: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.3. Neuromorphic Computing Market in Japan: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.4. Neuromorphic Computing Market in Singapore: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.5. Neuromorphic Computing Market in South Korea: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.6.6. Neuromorphic Computing Market in Other Asian Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
25.7. Data Triangulation and Validation

26. MARKET OPPORTUNITIES FOR NEUROMORPHIC COMPUTING IN MIDDLE EAST AND NORTH AFRICA (MENA)
26.1. Chapter Overview
26.2. Key Assumptions and Methodology
26.3. Revenue Shift Analysis
26.4. Market Movement Analysis
26.5. Penetration-Growth (P-G) Matrix
26.6. Neuromorphic Computing Market in Middle East and North Africa (MENA): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.1. Neuromorphic Computing Market in Egypt: Historical Trends (Since 2019) and Forecasted Estimates (Till 205)
26.6.2. Neuromorphic Computing Market in Iran: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.3. Neuromorphic Computing Market in Iraq: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.4. Neuromorphic Computing Market in Israel: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.5. Neuromorphic Computing Market in Kuwait: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.6. Neuromorphic Computing Market in Saudi Arabia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.7. Neuromorphic Computing Marke in United Arab Emirates (UAE): Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.6.8. Neuromorphic Computing Market in Other MENA Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
26.7. Data Triangulation and Validation

27. MARKET OPPORTUNITIES FOR NEUROMORPHIC COMPUTING IN LATIN AMERICA
27.1. Chapter Overview
27.2. Key Assumptions and Methodology
27.3. Revenue Shift Analysis
27.4. Market Movement Analysis
27.5. Penetration-Growth (P-G) Matrix
27.6. Neuromorphic Computing Market in Latin America: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.1. Neuromorphic Computing Market in Argentina: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.2. Neuromorphic Computing Market in Brazil: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.3. Neuromorphic Computing Market in Chile: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.4. Neuromorphic Computing Market in Colombia Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.5. Neuromorphic Computing Market in Venezuela: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.6.6. Neuromorphic Computing Market in Other Latin American Countries: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
27.7. Data Triangulation and Validation

28. MARKET OPPORTUNITIES FOR NEUROMORPHIC COMPUTING IN REST OF THE WORLD
28.1. Chapter Overview
28.2. Key Assumptions and Methodology
28.3. Revenue Shift Analysis
28.4. Market Movement Analysis
28.5. Penetration-Growth (P-G) Matrix
28.6. Neuromorphic Computing Market in Rest of the World: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.1. Neuromorphic Computing Market in Australia: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.2. Neuromorphic Computing Market in New Zealand: Historical Trends (Since 2019) and Forecasted Estimates (Till 2035)
28.6.3. Neuromorphic Computing Market in Other Countries
28.7. Data Triangulation and Validation

29. MARKET CONCENTRATION ANALYSIS: DISTRIBUTION BY LEADING PLAYERS
29.1. Leading Player 1
29.2. Leading Player 2
29.3. Leading Player 3
29.4. Leading Player 4
29.5. Leading Player 5
29.6. Leading Player 6
29.7. Leading Player 7
29.8. Leading Player 8

30. ADJACENT MARKET ANALYSIS

SECTION VII: STRATEGIC TOOLS

31. KEY WINNING STRATEGIES
32. PORTER’S FIVE FORCES ANALYSIS
33. SWOT ANALYSIS
34. VALUE CHAIN ANALYSIS

35. ROOTS STRATEGIC RECOMMENDATIONS
35.1. Chapter Overview
35.2. Key Business-related Strategies
35.2.1. Research & Development
35.2.2. Product Manufacturing
35.2.3. Commercialization / Go-to-Market
35.2.4. Sales and Marketing
35.3. Key Operations-related Strategies
35.3.1. Risk Management
35.3.2. Workforce
35.3.3. Finance
35.3.4. Others

SECTION VIII: OTHER EXCLUSIVE INSIGHTS

36. INSIGHTS FROM PRIMARY RESEARCH
37. REPORT CONCLUSION

SECTION IX: APPENDIX
38. TABULATED DATA
39. LIST OF COMPANIES AND ORGANIZATIONS
40. CUSTOMIZATION OPPORTUNITIES
41. ROOTS SUBSCRIPTION SERVICES
42. AUTHOR DETAILS