Neuromorphic Computing Chips Market - Forecasts From 2025 To 2030

The neuromorphic computing chips market is projected to grow at a CAGR of 52.95% during the projected period (2022-2030).

Neuromorphic circuits offer remarkable efficiency and performance for AI applications by simulating the neuronal architecture of the human brain.  These chips are perfect for machine learning, deep learning, and cognitive computing applications because of their ability to analyze complicated and real-time data.  Neuromorphic circuits are growing in demand as AI develops and spreads into a variety of industries, including robotics, healthcare, and driverless cars.  They are a key element in developing AI technologies because of their capacity to carry out fast calculations while using less energy.

Neuromorphic Computing Chips Market Overview & Scope

The neuromorphic computing chips market is segmented by:

• Technology: The neuromorphic computing chips market is segmented into CMOS technology, memristor technology, and others. The market for neuromorphic chips is based on complementary metal-oxide-semiconductor (CMOS) technology, which offers the fundamental components needed to create neuromorphic processors. The semiconductor industry makes extensive use of CMOS technology because of its efficiency, scalability, and affordability. CMOS-based neuromorphic devices are appropriate for various applications, such as edge computing, robotics, and artificial intelligence, since they can achieve great density and performance with little power consumption. The capabilities and market acceptance of neuromorphic devices are further enhanced by ongoing developments in CMOS technology, such as the creation of transistors that are smaller and more efficient.
• End-Use Industry: By end-use industry, the market is segmented into consumer electronics, healthcare, automotive, industrial, aerospace and defense, and telecommunications, among others. The growing integration of advanced driver assistance systems (ADAS) and autonomous vehicle technologies has made the automotive sector an important business segment. These applications benefit greatly from neuromorphic chips' real-time processing of sensory data from many sources, which facilitates adaptive learning and prompt decision-making. These chips can improve autonomous cars' situational awareness, traffic prediction, and object identification, making driving safer and more effective.
• Region:  The market is segmented into five major geographic regions, namely North America, South America, Europe, the Middle East, Africa, and Asia-Pacific. North America is anticipated to dominate the market, and it will be growing at the fastest CAGR.  

Top Trends Shaping the Neuromorphic Computing Chips Market

1. Emergence of Neuromorphic Systems with Edge Computing

• The growing use of edge computing is one of the key trends propelling the global neuromorphic computing market.  An increasing number of neuromorphic systems are being created and deployed on the edge, enabling quick data processing and instantaneous decision-making in applications like robots, autonomous vehicles, and the Internet of Things (IoT).

2. AI and ML Integration with Neuromorphic Computing

• The seamless integration of neuromorphic machines with AI and machine learning is another significant advancement.  The emergence of real-time analytics, predictive analytics, and adaptive learning in the domains of autonomous systems, security, and healthcare is a result of this combination, which enhances the capacity to manage vast, complicated datasets effectively.  

Neuromorphic Computing Chips Market Growth Drivers vs. Challenges

Opportunities:

• Demand for AI Solutions That Use Less Energy: The growing demand for energy-efficient computing paradigms is the main factor propelling the worldwide neuromorphic computing market's expansion. Since conventional hardware cannot afford the high-power consumption dynamics and real-time response of such AI technologies, neuromorphic systems that mimic the low-power processing associated with the brain have been developed to address the energy limitations of mechanical intelligence solutions, such as robots and self-driving cars.
• Improving Data Processing in Real Time: Applications of neuromorphic computing chips in the majority of industries demonstrate how effectively AI and machine learning have advanced over the years. Complex, fast data analysis and the decision-making processes needed for real-time applications, such as those in the Internet of Things (IoT), security, and medical, are made possible by the unique architecture of neuromorphic computing. Additionally, the availability of neuromorphic computers, which boost computing performance and reduce processing turnaround time, has increased as more industries use artificial intelligence.

Challenges:

• Standardization and Development Obstacles: Standardized programming tools and frameworks for neuromorphic systems are not advanced. Programmers accustomed to more conventional architectures may find neuromorphic computing challenging since it requires the usage of particular, unconventional programming paradigms. The lack of these tools slows down integration and development, which restricts adoption across sectors and applications.

Neuromorphic Computing Chips Market Regional Analysis

• North America: North America, especially the US and Canada, is distinguished by its leadership in the creation and uptake of new technologies, making up a major market share for industry. Considerable contributions from academic institutions and corporate firms have converted the United States into a major hub for neuromorphic chip research. Neuromorphic circuits that can provide high-performance and energy-efficient processing are in high demand due to the nation's significant emphasis on artificial intelligence, autonomous systems, and edge computing. Technological innovation and AI research developments in Canada are further helping to fuel the expansion of the neuromorphic chip market. The region's strong infrastructure, emphasis on next-generation computer technology, and investments in tech startups demonstrate its substantial market share worldwide.

Neuromorphic Computing Chips Market Competitive Landscape

The market is moderately fragmented, with many key players including Intel Corporation, IBM Corporation, Samsung Electronics Co., Ltd, and BrainChip, Inc.

• Product Innovation: In April 2024, Intel declared that it had constructed the largest neuromorphic system in the world. This large-scale neuromorphic system, code-named Hala Point, was first installed at Sandia National Laboratories. It makes use of Intel's Loihi 2 processor, supports research for future brain-inspired artificial intelligence (AI), and addresses issues with the sustainability and efficiency of current AI.
• Sustainable product launch: In May 2024, Large-scale neuromorphic chips with an innovative instruction set design and on-chip learning were created by SSD researchers. The development of specialized neuromorphic computing chips aims to better utilize the advantages of SNNs. These chips offer a potential remedy for storage and power limitations in the post-Moore era, marking a break from the conventional computing architecture.

Neuromorphic Computing Chips Market Segmentation:

By Technology

• CMOS technology
• Memristor technology
• Others

By End-Use Industry

• Consumer electronics
• Healthcare
• Automotive
• Industrial
• Aerospace 
• Defense

By Region

• North America
  • USA
  • Mexico
  • Others
• South America
  • Brazil
  • Argentina
  • Others
• Europe
  • United Kingdom
  • Germany
  • France
  • Spain
  • Others
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • Others
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Taiwan
  • Others

1. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK

5. NEUROMORPHIC COMPUTING CHIPS MARKET BY TECHNOLOGY

5.1. Introduction

5.2. CMOS technology

5.3. Memristor technology

5.4. Others

6. NEUROMORPHIC COMPUTING CHIPS MARKET BY END-USE INDUSTRY 

6.1. Introduction

6.2. Consumer electronics

6.3. Healthcare

6.4. Automotive

6.5. Industrial

6.6. Aerospace 

6.7. Defense

7. NEUROMORPHIC COMPUTING CHIPS MARKET BY GEOGRAPHY 

7.1. Introduction

7.2. North America

7.2.1. By Technology

7.2.2. By End-Use Industry

7.2.3. By Country

7.2.3.1. USA

7.2.3.2. Canada

7.2.3.3. Mexico

7.3. South America

7.3.1. By Technology

7.3.2. By End-Use Industry

7.3.3. By Country

7.3.3.1. Brazil

7.3.3.2. Argentina

7.3.3.3. Others

7.4. Europe

7.4.1. By Technology

7.4.2. By End-Use Industry

7.4.3. By Country

7.4.3.1. United Kingdom

7.4.3.2. Germany

7.4.3.3. France

7.4.3.4. Spain

7.4.3.5. Others

7.5. Middle East and Africa

7.5.1. By Technology

7.5.2. By End-Use Industry

7.5.3. By Country

7.5.3.1. Saudi Arabia

7.5.3.2. UAE

7.5.3.3. Others

7.6. Asia Pacific

7.6.1. By Technology

7.6.2. By End-Use Industry

7.6.3. By Country

7.6.3.1. China

7.6.3.2. Japan

7.6.3.3. India

7.6.3.4. South Korea

7.6.3.5. Taiwan

7.6.3.6. Others

8. COMPETITIVE ENVIRONMENT AND ANALYSIS

8.1. Major Players and Strategy Analysis

8.2. Market Share Analysis

8.3. Mergers, Acquisitions, Agreements, and Collaborations

8.4. Competitive Dashboard

9. COMPANY PROFILES

9.1. Intel Corporation

9.2. IBM Corporation

9.3. Samsung Electronics Co., Ltd

9.4. BrainChip, Inc.

9.5. Polyn Technology

9.6. SynSense AG

9.7. Qualcomm Incorporated

9.8. Micron Technology 

9.9. Hewlett Packard 

9.10. Brain Corporation 

10. APPENDIX

10.1. Currency 

10.2. Assumptions

10.3. Base and Forecast Years Timeline

10.4. Key benefits for the stakeholders

10.5. Research Methodology 

10.6. Abbreviations 

Intel Corporation

IBM Corporation

Samsung Electronics Co., Ltd

BrainChip, Inc.

Polyn Technology

SynSense AG

Qualcomm Incorporated

Micron Technology 

Hewlett Packard 

Brain Corporation