Neuromorphic Computing Market - 2024-2031
Description
MARKET SIZE AND OVERVIEW
The Global Neuromorphic Computing Market reached USD 48.3 million in 2022 and is projected to witness lucrative growth by reaching up to USD 6,801.9 million by 2030. The global neuromorphic computing market is expected to exhibit a CAGR of 91.5% during the forecast period (2024-2031). A computing paradigm known as neuromorphic computing’ uses specialized hardware and algorithms to mimic the organization and operation of the human brain.
It seeks to imitate the brain's neural networks' parallel processing, energy efficiency, and adaptability. Pattern recognition, sensory processing, and cognitive computing (https://www.datamintelligence.com/research-report/cognitive-computing-market) are all tasks that neuromorphic computing systems excel at because they are made to handle information in a way that is similar to how the brain does.
MARKET SCOPE
Metrics Details
CAGR 91.5%
Size Available for Years 2021-2030
Forecast Period 2023-2030
Data Availability Value (USD )
Segments Covered Offering, Deployment, Application, End User, and Region
Regions Covered North America, Europe, Asia-Pacific, South America, and Middle East & Africa
Fastest Growing Region Asia-Pacific
Largest Region North America
Report Insights Covered Competitive Landscape Analysis, Company Profile Analysis, Market Size, Share, Growth, Demand, Recent Developments, Mergers and Acquisitions, New Product Launches, Growth Strategies, Revenue Analysis, Porter’s Analysis, Pricing Analysis, Regulatory Analysis, Supply-Chain Analysis, and Orthopedic Surgery key Insights.
For more details on this report – Download Sample (https://www.datamintelligence.com/download-sample/neuromorphic-computing-market)
MARKET DYNAMICS
THE INCREASING APPLICATION OF NEUROMORPHIC COMPUTING IS DRIVING THE GLOBAL NEUROMORPHIC COMPUTING MARKET GROWTH.
In recent years, the science of neuromorphic computing has advanced quickly, with an increased emphasis on hardware design and dependability. As per the article published in Frontiers in 2022, researchers from a variety of fields, including computer science, electrical (https://www.datamintelligence.com/research-report/electrical-conduit-market) engineering, neuroscience, physics, and computer engineering have been drawn to the interdisciplinary field of neuromorphic research.
While neuromorphic computing is more concerned with application development, the goal of neuromorphic engineering is to mimic the behavior of biological brain networks in circuits and systems. A variety of bio-inspired neuron and network models have been included in digital, analog, and mixed-signal circuits and systems in recent years to mimic the operation of the brain. Despite these developments, neuromorphic computing is yet to reach its full potential.
THE GROWING IMPORTANCE OF NEUROMORPHIC COMPUTING IS DRIVING THE GLOBAL NEUROMORPHIC COMPUTING MARKET GROWTH.
Artificial intelligence algorithms because it effectively executes by mimicking the human brain's nerve structure, and the importance of neuromorphic computing has grown within the sector. Because of memory and processor limitations, traditional von Neumann computing is inefficient for machine learning. Machine learning has a unique workload that iterates simple computation with a lot of data, there should be a ton of data traffic between processors and memory.
The potential for neuromorphic computing to revolutionize fields like artificial intelligence (AI) and machine learning (ML) is one of its main advantages. Neuromorphic computing has gained importance in the industry because it effectively executes artificial intelligence algorithms by mimicking the human brain's nerve system. Neuromorphic systems are able to carry out difficult cognitive tasks more effectively and precisely than conventional computing architectures by utilizing the ideas of neural networks and brain-inspired computing.
Multiple neurons and synapses are used in neuromorphic computing systems to compute and store data, and a neural network is used to convey that data. As a result, this computing system can compute straightforward iterations efficiently for machine learning training.
COMPLEXITY AND LIMITED STANDARDIZATION ARE HAMPERING THE GLOBAL NEUROMORPHIC COMPUTING MARKET GROWTH.
The area of neuromorphic computing is rather complicated and necessitates specialized hardware and algorithms. Working with neuromorphic systems is tough for developers and researchers due to the lack of standardized frameworks and programming models. The adoption of neuromorphic computing may be slowed down and its accessibility may be restricted due to this complexity and lack of standardization.
In contrast to the robust ecosystem for conventional computer architectures, there are comparatively few software tools, libraries, and frameworks designed expressly for neuromorphic computing. The acceptance and development of neuromorphic applications may be hampered by the lack of developer-friendly tools and resources because such applications need specialized knowledge and resources.
MARKET SEGMENT ANALYSIS
The global neuromorphic computing market is segmented based on offering, deployment, application, end-user, and region.
THE HARDWARE SEGMENT IS EXPECTED TO HOLD A DOMINANT POSITION IN THE MARKET OVER THE FORECAST PERIOD.
The plastic hardware Neuromorphic Computing segment accounted for the highest market stake accounting for approximately 67.8% of the neuromorphic computing market in 2022. Since adding more neuromorphic chips implies increasing the potential number of neurons and synapses, neuromorphic computers are designed to be intrinsically scalable.
In order to run larger and larger networks, it is possible to treat a number of physical neuromorphic chips as a single huge neuromorphic implementation. Numerous large-scale neuromorphic hardware devices, notably SpiNNaker and Loihi, have been used to successfully achieve this.
Source: DataM Intelligence Analysis (2023)
MARKET GEOGRAPHICAL SHARE
NORTH AMERICA HOLDS A DOMINANT POSITION IN THE GLOBAL NEUROMORPHIC COMPUTING MARKET.
North America is estimated to hold around 41.3% of the total market share throughout the forecast period. Active market players boost regional growth over the forecast period. For instance, researchers at Berkeley Lab are developing an oscillatory collective network dynamics-based framework for neuromorphic computing, in which each node in the network acts as an oscillator (a dynamic process), and the network topology specifies which oscillators interact with one another.
This method enables dynamically self-reconfiguring neural networks to process information in a flexible and adaptive manner, which is helpful for managing distributed computing on a big scale. They are also investigating the tradeoffs between the price of implementing individual neurons and their computational complexity.
Source: DataM Intelligence Analysis (2023)
MARKET COMPANIES
The major global players in the market include Brain Corporation, CEA-Leti (https://www.leti-cea.com/cea-tech/leti/english/Pages/What), General Vision, inc, Hewlett Packard Company, HRL Laboratories, LLC, International Business Machines Corporation, Intel Corporation, Knowm Inc, Qualcomm Technologies (http://qualcomm.com/news/onq/2013/10/introducing-qualcomm-zeroth-processors-brain-inspired-computing), Inc, and Samsung Electronics Co., Ltd among others.
COVID-19 IMPACT ON NEUROMORPHIC COMPUTING MARKET
RUSSIA-UKRAINE CONFLICT ANALYSIS
The Russia-Ukraine war may affect the neuromorphic computing market due to investor confidence and research activities being impacted during times of geopolitical instability and economic uncertainty. Conflicts on the global stage can breed uncertainty and undermine investor confidence. Investor caution may prevail, which could have an impact on market funding, R&D, and development plans for neuromorphic computing. Neuromorphic computer technology companies may have trouble attracting investment and maintaining growth in an unstable climate.
ARTIFICIAL INTELLIGENCE ANALYSIS:
The market for neuromorphic computing is significantly influenced by artificial intelligence (AI). The goal of neuromorphic computing, an area of artificial intelligence, is to use specialized hardware and algorithms to mimic the structure and operation of the human brain. In neuromorphic computer systems, neural networks are modeled and simulated using AI techniques, particularly machine learning. Researchers and developers can use these models to simulate the actions of biological neurons and synapses.
KEY DEVELOPMENTS
• In June 2023, researchers at Los Alamos National Laboratory created a new interface-type memristive device in an effort to mimic that still-unmatched computing ability. Their findings imply that this device can be utilized to create artificial synapses for next-generation neuromorphic computing.
WHY PURCHASE THE REPORT?
• To visualize the global neuromorphic computing market segmentation based on the offering, deployment, end-user, and region, as well as understand key commercial assets and players.
• Identify commercial opportunities by analyzing trends and co-development.
• Excel data sheet with numerous data points of neuromorphic computing market-level with all segments.
• PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
• Product mapping available as Excel consisting of key products of all the major players.
THE GLOBAL NEUROMORPHIC COMPUTING MARKET REPORT WOULD PROVIDE APPROXIMATELY 69 TABLES, 67 FIGURES, AND 195 PAGES.
TARGET AUDIENCE 2023
• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies
The Global Neuromorphic Computing Market reached USD 48.3 million in 2022 and is projected to witness lucrative growth by reaching up to USD 6,801.9 million by 2030. The global neuromorphic computing market is expected to exhibit a CAGR of 91.5% during the forecast period (2024-2031). A computing paradigm known as neuromorphic computing’ uses specialized hardware and algorithms to mimic the organization and operation of the human brain.
It seeks to imitate the brain's neural networks' parallel processing, energy efficiency, and adaptability. Pattern recognition, sensory processing, and cognitive computing (https://www.datamintelligence.com/research-report/cognitive-computing-market) are all tasks that neuromorphic computing systems excel at because they are made to handle information in a way that is similar to how the brain does.
MARKET SCOPE
Metrics Details
CAGR 91.5%
Size Available for Years 2021-2030
Forecast Period 2023-2030
Data Availability Value (USD )
Segments Covered Offering, Deployment, Application, End User, and Region
Regions Covered North America, Europe, Asia-Pacific, South America, and Middle East & Africa
Fastest Growing Region Asia-Pacific
Largest Region North America
Report Insights Covered Competitive Landscape Analysis, Company Profile Analysis, Market Size, Share, Growth, Demand, Recent Developments, Mergers and Acquisitions, New Product Launches, Growth Strategies, Revenue Analysis, Porter’s Analysis, Pricing Analysis, Regulatory Analysis, Supply-Chain Analysis, and Orthopedic Surgery key Insights.
For more details on this report – Download Sample (https://www.datamintelligence.com/download-sample/neuromorphic-computing-market)
MARKET DYNAMICS
THE INCREASING APPLICATION OF NEUROMORPHIC COMPUTING IS DRIVING THE GLOBAL NEUROMORPHIC COMPUTING MARKET GROWTH.
In recent years, the science of neuromorphic computing has advanced quickly, with an increased emphasis on hardware design and dependability. As per the article published in Frontiers in 2022, researchers from a variety of fields, including computer science, electrical (https://www.datamintelligence.com/research-report/electrical-conduit-market) engineering, neuroscience, physics, and computer engineering have been drawn to the interdisciplinary field of neuromorphic research.
While neuromorphic computing is more concerned with application development, the goal of neuromorphic engineering is to mimic the behavior of biological brain networks in circuits and systems. A variety of bio-inspired neuron and network models have been included in digital, analog, and mixed-signal circuits and systems in recent years to mimic the operation of the brain. Despite these developments, neuromorphic computing is yet to reach its full potential.
THE GROWING IMPORTANCE OF NEUROMORPHIC COMPUTING IS DRIVING THE GLOBAL NEUROMORPHIC COMPUTING MARKET GROWTH.
Artificial intelligence algorithms because it effectively executes by mimicking the human brain's nerve structure, and the importance of neuromorphic computing has grown within the sector. Because of memory and processor limitations, traditional von Neumann computing is inefficient for machine learning. Machine learning has a unique workload that iterates simple computation with a lot of data, there should be a ton of data traffic between processors and memory.
The potential for neuromorphic computing to revolutionize fields like artificial intelligence (AI) and machine learning (ML) is one of its main advantages. Neuromorphic computing has gained importance in the industry because it effectively executes artificial intelligence algorithms by mimicking the human brain's nerve system. Neuromorphic systems are able to carry out difficult cognitive tasks more effectively and precisely than conventional computing architectures by utilizing the ideas of neural networks and brain-inspired computing.
Multiple neurons and synapses are used in neuromorphic computing systems to compute and store data, and a neural network is used to convey that data. As a result, this computing system can compute straightforward iterations efficiently for machine learning training.
COMPLEXITY AND LIMITED STANDARDIZATION ARE HAMPERING THE GLOBAL NEUROMORPHIC COMPUTING MARKET GROWTH.
The area of neuromorphic computing is rather complicated and necessitates specialized hardware and algorithms. Working with neuromorphic systems is tough for developers and researchers due to the lack of standardized frameworks and programming models. The adoption of neuromorphic computing may be slowed down and its accessibility may be restricted due to this complexity and lack of standardization.
In contrast to the robust ecosystem for conventional computer architectures, there are comparatively few software tools, libraries, and frameworks designed expressly for neuromorphic computing. The acceptance and development of neuromorphic applications may be hampered by the lack of developer-friendly tools and resources because such applications need specialized knowledge and resources.
MARKET SEGMENT ANALYSIS
The global neuromorphic computing market is segmented based on offering, deployment, application, end-user, and region.
THE HARDWARE SEGMENT IS EXPECTED TO HOLD A DOMINANT POSITION IN THE MARKET OVER THE FORECAST PERIOD.
The plastic hardware Neuromorphic Computing segment accounted for the highest market stake accounting for approximately 67.8% of the neuromorphic computing market in 2022. Since adding more neuromorphic chips implies increasing the potential number of neurons and synapses, neuromorphic computers are designed to be intrinsically scalable.
In order to run larger and larger networks, it is possible to treat a number of physical neuromorphic chips as a single huge neuromorphic implementation. Numerous large-scale neuromorphic hardware devices, notably SpiNNaker and Loihi, have been used to successfully achieve this.
Source: DataM Intelligence Analysis (2023)
MARKET GEOGRAPHICAL SHARE
NORTH AMERICA HOLDS A DOMINANT POSITION IN THE GLOBAL NEUROMORPHIC COMPUTING MARKET.
North America is estimated to hold around 41.3% of the total market share throughout the forecast period. Active market players boost regional growth over the forecast period. For instance, researchers at Berkeley Lab are developing an oscillatory collective network dynamics-based framework for neuromorphic computing, in which each node in the network acts as an oscillator (a dynamic process), and the network topology specifies which oscillators interact with one another.
This method enables dynamically self-reconfiguring neural networks to process information in a flexible and adaptive manner, which is helpful for managing distributed computing on a big scale. They are also investigating the tradeoffs between the price of implementing individual neurons and their computational complexity.
Source: DataM Intelligence Analysis (2023)
MARKET COMPANIES
The major global players in the market include Brain Corporation, CEA-Leti (https://www.leti-cea.com/cea-tech/leti/english/Pages/What), General Vision, inc, Hewlett Packard Company, HRL Laboratories, LLC, International Business Machines Corporation, Intel Corporation, Knowm Inc, Qualcomm Technologies (http://qualcomm.com/news/onq/2013/10/introducing-qualcomm-zeroth-processors-brain-inspired-computing), Inc, and Samsung Electronics Co., Ltd among others.
COVID-19 IMPACT ON NEUROMORPHIC COMPUTING MARKET
RUSSIA-UKRAINE CONFLICT ANALYSIS
The Russia-Ukraine war may affect the neuromorphic computing market due to investor confidence and research activities being impacted during times of geopolitical instability and economic uncertainty. Conflicts on the global stage can breed uncertainty and undermine investor confidence. Investor caution may prevail, which could have an impact on market funding, R&D, and development plans for neuromorphic computing. Neuromorphic computer technology companies may have trouble attracting investment and maintaining growth in an unstable climate.
ARTIFICIAL INTELLIGENCE ANALYSIS:
The market for neuromorphic computing is significantly influenced by artificial intelligence (AI). The goal of neuromorphic computing, an area of artificial intelligence, is to use specialized hardware and algorithms to mimic the structure and operation of the human brain. In neuromorphic computer systems, neural networks are modeled and simulated using AI techniques, particularly machine learning. Researchers and developers can use these models to simulate the actions of biological neurons and synapses.
KEY DEVELOPMENTS
• In June 2023, researchers at Los Alamos National Laboratory created a new interface-type memristive device in an effort to mimic that still-unmatched computing ability. Their findings imply that this device can be utilized to create artificial synapses for next-generation neuromorphic computing.
WHY PURCHASE THE REPORT?
• To visualize the global neuromorphic computing market segmentation based on the offering, deployment, end-user, and region, as well as understand key commercial assets and players.
• Identify commercial opportunities by analyzing trends and co-development.
• Excel data sheet with numerous data points of neuromorphic computing market-level with all segments.
• PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
• Product mapping available as Excel consisting of key products of all the major players.
THE GLOBAL NEUROMORPHIC COMPUTING MARKET REPORT WOULD PROVIDE APPROXIMATELY 69 TABLES, 67 FIGURES, AND 195 PAGES.
TARGET AUDIENCE 2023
• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies
Table of Contents
180 Pages
- 1. Methodology and Scope
- 1.1. Research Methodology
- 1.2. Research Objective and Scope of the Report
- 2. Definition and Overview
- 3. Executive Summary
- 3.1. Snippet by Offering
- 3.2. Snippet by Deployment
- 3.3. Snippet by Application
- 3.4. Snippet by End User
- 3.5. Snippet by Region
- 4. Dynamics
- 4.1. Impacting Factors
- 4.1.1. Drivers
- 4.1.1.1. The increasing use of neuromorphic technology in deep learning applications
- 4.1.2. Restraints
- 4.1.2.1. Lack of R & D investment
- 4.1.3. Opportunity
- 4.1.3.1. Technological advances
- 4.1.4. Impact Analysis
- 5. Industry Analysis
- 5.1. Porter's 5 Forces Analysis
- 5.2. Supply Chain Analysis
- 5.3. Unmet Needs
- 5.4. Regulatory Analysis
- 6. COVID-19 Analysis
- 6.1. COVID-19 Analysis
- 6.2. Analysis of COVID-19
- 6.2.1. Scenario Before COVID-19
- 6.2.2. Scenario During COVID-19
- 6.2.3. Scenario Post COVID-19 & Future Scenario
- 6.3. Pricing Dynamics Amid COVID-19
- 6.4. Demand-Supply Spectrum
- 6.5. Government Initiatives Related to the Market During the Pandemic
- 6.6. Manufacturers’ Strategic Initiatives
- 6.7. Conclusion
- 7. Russia-Ukraine War Analysis
- 8. Artificial Intelligence Analysis
- 9. By Offering
- 9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 9.1.2. Market Attractiveness Index, By Offering
- 9.2. Hardware*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
- 9.3. Software
- 10. By Deployment
- 10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 10.1.2. Market Attractiveness Index, By Deployment
- 10.2. Edge Computing*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
- 10.3. Cloud Computing
- 11. By Application
- 11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.1.2. Market Attractiveness Index, By Application
- 11.2. Image Recognition*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
- 11.3. Signal Recognition
- 11.4. Data Mining
- 11.5. Others
- 12. By End-user
- 12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-user
- 12.1.2. Market Attractiveness Index, By End-user
- 12.2. Consumer Electronics*
- 12.2.1. Introduction
- 12.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
- 12.3. Automotive
- 12.4. IT & Telecom
- 12.5. Aerospace & Defense
- 12.6. Healthcare
- 12.7. Others
- 13. By Region
- 13.1. Introduction
- 13.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 13.1.2. Market Attractiveness Index, By Region
- 13.2. North America
- 13.2.1. Introduction
- 13.2.2. Key Region-Specific Dynamics
- 13.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-user
- 13.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
- 13.2.7.1. The U.S.
- 13.2.7.2. Canada
- 13.2.7.3. Mexico
- 13.3. Europe
- 13.3.1. Introduction
- 13.3.2. Key Region-Specific Dynamics
- 13.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-user
- 13.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
- 13.3.7.1. Germany
- 13.3.7.2. The U.K.
- 13.3.7.3. France
- 13.3.7.4. Italy
- 13.3.7.5. Spain
- 13.3.7.6. Rest of Europe
- 13.4. South America
- 13.4.1. Introduction
- 13.4.2. Key Region-Specific Dynamics
- 13.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-user
- 13.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
- 13.4.7.1. Brazil
- 13.4.7.2. Argentina
- 13.4.7.3. Rest of South America
- 13.5. Asia-Pacific
- 13.5.1. Introduction
- 13.5.2. Key Region-Specific Dynamics
- 13.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-user
- 13.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
- 13.5.7.1. China
- 13.5.7.2. India
- 13.5.7.3. Japan
- 13.5.7.4. Australia
- 13.5.7.5. Rest of Asia-Pacific
- 13.6. Middle East and Africa
- 13.6.1. Introduction
- 13.6.2. Key Region-Specific Dynamics
- 13.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-user
- 14. Competitive Landscape
- 14.1. Competitive Scenario
- 14.2. Product Benchmarking
- 14.3. Company Share Analysis
- 14.4. Key Developments and Strategies
- 15. Company Profiles
- 15.1. Brain Corporation*
- 15.1.1. Company Overview
- 15.1.2. Product Portfolio and Description
- 15.1.3. Financial Overview
- 15.1.4. Key Developments
- 15.2. CEA-Leti
- 15.3. General Vision, inc
- 15.4. Hewlett Packard Company
- 15.5. HRL Laboratories, LLC
- 15.6. International Business Machines Corporation
- 15.7. Intel Corporation
- 15.8. Knowm Inc
- 15.9. Qualcomm Technologies, Inc
- 15.10. Samsung Electronics Co., Ltd (*LIST NOT EXHAUSTIVE)
- 16. Appendix
- 16.1. About Us and Services
- 16.2. Contact Us
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