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Self Learning Neuromorphic Chip Market Trends

ID: MRFR//2974-HCR | 100 Pages | Author: Shubham Munde| July 2024

Global Self-Learning Neuromorphic Chip Market Overview:

Self-Learning Neuromorphic Chip Market Size was valued at USD 0.63 Billion in 2023. The Self-Learning Neuromorphic Chip industry is projected to grow from USD 0.79695 Billion in 2024 to USD 4.14 Billion by 2032, exhibiting a compound annual growth rate (CAGR) of 22.87% during the forecast period (2024 - 2032). Decreasing knowledge complexity in designing chips and the rise in machine learning technology are the key market drivers enhancing market growth.

Global Self-Learning Neuromorphic Chip Market Overview

Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review

Self-Learning Neuromorphic Chip Market Trends

  • Growing demand for edge computing is driving the market growth

Market CAGR for self-learning neuromorphic chips is driven by the rising demand for edge computing. Edge computing includes processing data closer to the source, reducing latency, and improving real-time decision-making capabilities. Self-learning neuromorphic chips, which can process large amounts of data in parallel and make autonomous decisions, are well-suited for edge computing applications. As the Internet of Things grows continually, there is a rising need for intelligent edge devices capable of processing and analyzing data locally without relying heavily on cloud computing. Self-learning neuromorphic chips can enable these edge devices to perform major tasks such as object recognition, anomaly detection, and predictive maintenance. The demand for edge computing coupled with the capabilities of self-learning neuromorphic chips is expected to drive market growth in the coming years.

Neuromorphic chips are designed to replicate the complex neural networks of the human brain, enabling them to process information more efficiently and effectively. Researchers and chip manufacturers have made significant progress in developing advanced architectures and designs that can better emulate the brain's functionalities in recent years. One notable development is the introduction of spiking neural networks (SNNs), which are more biologically realistic than traditional artificial neural networks. SNNs allow for asynchronous processing, event-driven computation, and low-power operation, making them ideal for self-learning neuromorphic chips. These advancements in architecture and design are driving the adoption of self-learning neuromorphic chips across various applications, such as pattern recognition, real-time data processing, and adaptive control systems.

The trend impacting the Self-Learning Neuromorphic Chip Market is the integration of neuromorphic chips in autonomous systems. Autonomous systems, including autonomous vehicles, drones, and robotics, require high-performance computing capabilities to navigate and interact with the environment in real time. Self-learning neuromorphic chips offer a promising solution due to their low power consumption, parallel processing, and adaptive learning capabilities. The ability of self-learning neuromorphic chips to continuously learn and adapt to new situations makes them ideal for autonomous systems operating in dynamic and unpredictable environments. These chips can enable autonomous systems to perform tasks such as object detection, path planning, and decision-making with improved efficiency and accuracy. As the demand for autonomous systems continues to rise, the integration of self-learning neuromorphic chips is expected to grow significantly.

The Self-Learning Neuromorphic Chip Market is witnessing significant trends shaping its growth and adoption across various industries. Advancements in architecture and design, increasing demand for edge computing, and the integration of neuromorphic chips in autonomous systems are three key trends driving market growth. As these trends continue to evolve, self-learning neuromorphic chips are likely to play a crucial role in advancing AI capabilities and powering future intelligent systems, driving the Self-Learning Neuromorphic Chip market revenue.

Self-Learning Neuromorphic Chip Market Segment Insights:

Self-Learning Neuromorphic Chip Vertical Insights

The Self-Learning Neuromorphic Chip Market segmentation, based on vertical, includes power & energy, media & entertainment, smartphones, healthcare, automotive, consumer electronics, aerospace, and defense. The power & energy segment dominated the market. The power and energy sector can benefit from self-learning neuromorphic chips in various ways. These chips can be used for intelligent energy management, predictive maintenance, and optimization of power grid operations. They enable efficient energy consumption, enhance grid stability, and improve overall power system reliability.

Self-Learning Neuromorphic Chip Application Insights

The Self-Learning Neuromorphic Chip Market segmentation, based on application, includes data mining, signal recognition, and image recognition. The data mining category generated the most income. These chips are utilized in data mining and analytics applications to process huge amounts of data and extract valuable insights. They enable real-time analysis, anomaly detection, and predictive modeling, benefiting various industries, including finance, e-commerce, and marketing.

Figure 1: Self-Learning Neuromorphic Chip Market, by Application, 2022 & 2032 (USD Billion)

Self-Learning Neuromorphic Chip Market, by Application, 2022 & 2032

Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review

Self-Learning Neuromorphic Chip Regional Insights

By region, the study provides market insights into North America, Europe, Asia-Pacific, and the Rest of the World. The North American Self-Learning Neuromorphic Chip market area will dominate this market due to the strong presence of leading technology companies, and research institutions focused on AI and ML and due to its robust ecosystem of chip manufacturers, research organizations, and AI startups. In addition, the increasing adoption of self-learning neuromorphic chips in applications such as autonomous vehicles, medical diagnostics, and defense systems are driving market growth in North America.

Further, the major countries studied in the market report are The US, Canada, German, France, the UK, Italy, Spain, China, Japan, India, Australia, South Korea, and Brazil.

Figure 2: Self-Learning Neuromorphic Chip Market SHARE BY REGION 2022 (USD Billion)

Self-Learning Neuromorphic Chip Market SHARE BY REGION 2022

Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review

Europe's Self-Learning Neuromorphic Chip market accounts for the second-largest market share due to the well-established semiconductor industry and a strong focus on AI research and development. The European Union's initiatives and funding support for AI technologies have further propelled the market growth in this region. The demand for self-learning neuromorphic chips in applications like smart cities, industrial automation, and energy management systems is driving market growth in Europe. Further, the German Self-Learning Neuromorphic Chip market held the largest market share, and the UK Self-Learning Neuromorphic Chip market was the fastest-growing market in the European region.

The Asia-Pacific Self-Learning Neuromorphic Chip Market is expected to grow fastest from 2024 to 2032. The government's support and initiatives to develop AI-based applications are due to it. The region's large population, rising disposable income, and increasing adoption of advanced technologies fuel the demand for self-learning neuromorphic chips. Industries such as robotics, healthcare, and consumer electronics are the market drivers in APAC. Moreover, China’s Self-Learning Neuromorphic Chip market held the largest market share, and the Indian Self-Learning Neuromorphic Chip market was the fastest-growing market in the Asia-Pacific region.

Self-Learning Neuromorphic Chip Key Market Players & Competitive Insights

Leading market players are investing heavily in research and development to expand their product lines, which will help the Self-Learning Neuromorphic Chip market grow even more. Market participants are also undertaking various strategic activities to expand their global footprint, with important market developments including new product launches, contractual agreements, mergers and acquisitions, higher investments, and collaboration with other organizations. To expand and survive in a more competitive and rising market climate, the Self-Learning Neuromorphic Chip industry must offer cost-effective items.

Manufacturing locally to minimize operational costs is one of the key business tactics manufacturers use in the global Self-Learning Neuromorphic Chip industry to benefit clients and increase the market sector. In recent years, the Self-Learning Neuromorphic Chip industry has offered some of the most significant medical advantages. Major players in the Self-Learning Neuromorphic Chip market, including Qualcomm (US), Numenta (US), Samsung Group (South Korea), IBM (US), Hewlett Packard (US), Brain chip Holdings Ltd. (US), HRL Laboratories (US), Applied Brain Research Inc. (US), General Vision (US), Intel Corporation (US), and others, are attempting to increase market demand by investing in research and development operations.

Intel Corporation, also known as Intel, founded in 1968 in Santa Clara, California, United States, is an American international technology company. It is one of the world's largest semiconductor chip manufacturers and is one of the developers of various series of instruction sets found in personal computers. It supplies microprocessors for computer system manufacturers and manufactures motherboard chipsets, integrated circuits, flash memory, embedded processors, and many more devices related to communications and computing. In October 2022, Intel announced a three-year agreement with Şandia National Laboratories (Sandia), US, to explore the value of neuromorphic computing for scaled-up computational problems. This agreement includes continued large-scale neuromorphic research on Intel's upcoming next-generation neuromorphic architecture and Intel's largest neuromorphic research system to date, which exceeds more than 1 billion neurons in computational capacity.

OPPO, founded in 2004, and located in Dongguan, Guangdong, China, is a Chinese consumer electronics manufacturing company. Its products include smartphones, smart devices, audio devices, power banks, and many more electronic products. The company has expanded in 50 countries all over the world. In November 2022, OPPO announced its collaboration with Qualcomm Technologies in ray tracing graphics for mobile devices. The company planned to implement Google Vertex Al Neural Architecture Search (Google NAS) on a smartphone for the first time. The unique solution concentrates on boosting the energy efficiency and latency of Al processing on mobile devices. Further, OPPO claims that its Find X flagship smartphone will be the first to get Qualcomm's latest flagship processor, Snapdragon 8 Gen 2 chipset.

Key Companies in the Self-Learning Neuromorphic Chip market include

  • Qualcomm (US)

  • Numenta (US)

  • Samsung Group (South Korea)

  • IBM (US)

  • Hewlett Packard (US)

  • Brainchip Holdings Ltd. (US)

  • HRL Laboratories (US)

  • Applied Brain Research Inc. (US)

  • General Vision (US)

  • Intel Corporation (US)

Self-Learning Neuromorphic Chip Industry Developments

May 2024: BrainChip is launching two “Akida Development Kits” for its self-learning low-power “Akida NSoC” neural networking chip designed for edge AI. One uses a Raspberry Pi CM4, while the other employs a Shuttle PC system based on Comet Lake-S processors. Two of its development kits that demonstrate its Akida neural networking processor (Akida NSoC) are now available for pre-order from BrainChip Holdings: the Linux-driven $4. 995 Akida Development Kit – Raspberry Pi and Linux/Win 10 compatible $9. 995 Akida Development Kit – Shuttle PC. Both implement Akida NSoC through a mini-PCIe module equipped with BrainChip’s AKD1000 silicon. The spiking neural networks (SNNs) enabled by this neuromorphic event-based Al processor called the Akida NSoC mimic brain processing primarily in terms of their ability to spike processes.

March 2024: Researchers from Tohoku University have developed a theoretical framework aimed at an advanced spin wave reservoir computing (RC) system using spintronics, which could save energy and space while providing more computational power than any other system of its size. This breakthrough brings us closer than ever before to achieving energy-efficient, nanoscale computing with unparalleled computational power. Brain-like Computing: The Ultimate Goal Of Artificial Intelligence.

October 2023: Belgian-based SpaceTech start-up EDGX and BrainChip Holdings Ltd, the first-ever commercial producer of ultra-low power fully digital event-based neuromorphic AI IP, announced a cooperation agreement targeted at developing data processing units for extreme environments. Space infrastructure has become increasingly important to us in our daily lives. Satellite-based services are essential for global positioning systems (GPS), weather forecasting, secure communications, climate monitoring, and emergency response during natural disasters, among other things. With the aim of making the space industry a self-sustaining economy, there’s been a boom in satellite launches. However, EDGX did not recognize product-driven innovation within this sector until it began acknowledging the growing demand and the opportunities available.

January 2023: IBM launched an energy-efficient Al chip with 7nm technology. The Al hardware accelerator chip supports various model types while achieving leading-edge power efficiency. The chip technology can be scaled and used for commercial applications to train large-scale models in the cloud for security and privacy efforts by bringing training closer to the end and data closer to the source.
June 2022: China's Tsinghua University Center for Brain-Inspired Computing Research researchers created a neuromorphic chip that consumes less power than a conventional NVIDIA chip designed for Al applications. Tianjicat used slightly more than half the power of an identical NVIDIA chip-based robot. They also discovered that their neuromorphic chip-based robot had 79 times less latency than the NVIDIA-based system, allowing it to make decisions much faster.

Self-Learning Neuromorphic Chip Market Segmentation:

Self-Learning Neuromorphic Chip Vertical Outlook

  • Power & Energy

  • Media & Entertainment

  • Smartphones

  • Healthcare

  • Automotive

  • Consumer Electronics

  • Aerospace

  • Defense

Self-Learning Neuromorphic Chip Application Outlook

  • Data Mining

  • Signal Recognition

  • Image Recognition

Self-Learning Neuromorphic Chip Regional Outlook

  • North America

    • US

    • Canada

  • Europe

    • Germany

    • France

    • UK

    • Italy

    • Spain

    • Rest of Europe

  • Asia-Pacific

    • China

    • Japan

    • India

    • Australia

    • South Korea

    • Australia

    • Rest of Asia-Pacific

  • Rest of the World

    • Middle East

    • Africa

    • Latin America

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