세계의 뉴로모픽 컴퓨팅 시장(-2035년) : 제공 유형별, 용도 유형별, 전개 방식별, 최종사용자 유형별, 지역별, 산업 동향, 예측
Neuromorphic Computing Market, Till 2035: Distribution by Type of Offering, Type of Application, Type of Deployment, Type of End User, and Geographical Regions: Industry Trends and Global Forecasts
상품코드:1803897
리서치사:Roots Analysis
발행일:On Demand Report
페이지 정보:영문 177 Pages
라이선스 & 가격 (부가세 별도)
한글목차
세계 뉴로모픽 컴퓨팅 시장 규모는 현재 26억 달러에서 2035년까지 614억 8,000만 달러에 달할 것으로 예상되며, 2035년까지 예측 기간 동안 33.32%의 연평균 성장률을 보일 것으로 예상됩니다.
뉴로모픽 컴퓨팅 시장 : 성장과 동향
뉴로모픽 컴퓨팅은 인간의 뇌 기능을 모방한 컴퓨팅 패러다임입니다. 일반적으로 뇌의 신경 구조와 시냅스를 에뮬레이트하도록 설계된 하드웨어와 소프트웨어를 모두 포함하며, 보다 자연스럽고 효율적인 정보 처리를 가능하게 합니다. 최초의 실리콘 뉴런과 시냅스는 1980년 뉴로모픽 컴퓨팅 모델을 확립한 미샤 마호워드와 카버 미드에 의해 만들어졌습니다. 이 접근법은 상호연결된 뉴런과 시냅스 네트워크를 통해 뇌가 정보를 병렬적으로 처리하고 뉴런 간의 통신을 촉진하기 위해 화학적, 전기적 신호를 전달한다는 생물학적 방법을 기반으로 합니다.
이 점에서 스파이킹 신경망(SNN)은 신경모방 컴퓨팅의 기본 개념을 나타내며, 생물학적 시스템이 어떻게 통신하는지를 반영하고 있습니다. SNN은 스파이크하는 시냅스와 인공 뉴런으로 구성되며, 연속적인 동기 신호에 의존하는 기존의 인공신경망(ANN)과는 다릅니다. 대신 SNN은 데이터 처리에 스파이크를 사용하여 실시간 에지 애플리케이션의 전력 효율을 향상시킵니다.
이 틀 안에서 뉴로모픽 컴퓨팅의 하드웨어는 뇌와 같은 처리를 재현하도록 설계된 전용 칩이 중요한 역할을 담당하고 있습니다. 이러한 뉴로모픽 칩은 뉴로모픽 원리를 기반으로 작동하며, 기존의 실리콘 기반 아키텍처보다 인식, 학습, 의사결정 등 다양한 AI 작업을 보다 효율적으로 수행합니다. 이 첨단 컴퓨팅 기술을 통해 산업계는 보다 높은 효율성과 정확도로 복잡한 작업을 수행할 수 있는 기계를 개발할 수 있게 되었습니다.
뉴로모픽 시스템의 목적은 전력 소비를 크게 줄인 상태에서 작동하는 것으로, 모바일 기기, 엣지 컴퓨팅 솔루션, 센서 네트워크와 같은 저전력 애플리케이션에 적합합니다. 또한, 데이터를 병렬로 처리하고, 실시간 정보를 처리하며, 확장성을 가지고 적응적으로 학습할 수 있는 능력은 AI, 로봇공학, 의료, 에너지 절약 컴퓨팅 등 다양한 분야에서 그 중요성이 강조되고 있습니다. AI와 머신러닝에 대한 수요가 증가하고 의료 분야에서 뉴로모픽 시스템의 통합이 진행됨에 따라, 뉴로모픽 컴퓨팅 시장은 예측 기간 동안 크게 성장할 것으로 예상됩니다.
세계의 뉴로모픽 컴퓨팅 시장에 대해 조사 분석했으며, 시장 규모 추정과 기회 분석, 경쟁 상황, 기업 프로파일, 메가트렌드 등의 정보를 전해드립니다.
목차
섹션 1 보고서 개요
제1장 서문
제2장 조사 방법
제3장 시장 역학
제4장 거시경제 지표
섹션 2 정성적 인사이트
제5장 주요 요약
제6장 소개
제7장 규제 시나리오
섹션 3 시장 개요
제8장 주요 기업의 종합적인 데이터베이스
제9장 경쟁 구도
제10장 화이트 스페이스 분석
제11장 기업 경쟁력 분석
제12장 뉴로모픽 컴퓨팅 시장의 스타트업 생태계
섹션 4 기업 개요
제13장 기업 개요
분석 개요
Accenture
BrainChip Holdings
Cadence Design Systems
CEA-Leti
General Vision
Gr AI Matter Labs
Hewlett Packard
HRL Laboratories
IBM
Innatera Nanosystems
Instar Robotics
Intel
Known
Koniku
Numenta
Qualcomm
Samsung Electronics
SK Hynix
NVIDIA
SynSense
Vicarious
섹션 5 시장 동향
제14장 메가트렌드 분석
제15장 미충족 수요 분석
제16장 특허 분석
제17장 최근 발전
섹션 6 시장 기회 분석
제18장 세계의 뉴로모픽 컴퓨팅 시장
제19장 시장 기회 : 제공 유형별
제20장 시장 기회 : 용도 유형별
제21장 시장 기회 : 전개 방식별
제22장 시장 기회 : 최종사용자 유형별
제23장 북미의 뉴로모픽 컴퓨팅 시장 기회
제24장 유럽의 뉴로모픽 컴퓨팅 시장 기회
제25장 아시아의 뉴로모픽 컴퓨팅 시장 기회
제26장 중동 및 북아프리카(MENA)의 뉴로모픽 컴퓨팅 시장 기회
제27장 라틴아메리카의 뉴로모픽 컴퓨팅 시장 기회
제28장 기타 지역의 뉴로모픽 컴퓨팅 시장 기회
제29장 시장 집중 분석 : 주요 기업별
제30장 인접 시장 분석
섹션 7 전략적 툴
제31장 승리의 열쇠가 되는 전략
제32장 Porter's Five Forces 분석
제33장 SWOT 분석
제34장 밸류체인 분석
제35장 Roots의 전략적 제안
섹션 8 기타 독점적 인사이트
제36장 1차 조사에서 인사이트
제37장 보고서 결론
섹션 9 부록
KSM
영문 목차
영문목차
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.
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
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15% Free Content Customization
Detailed Report Walkthrough Session with Research Team
Free Updated report if the report is 6-12 months old or older
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
