Edge Artificial Intelligence Chip  Market Report: Trends, Forecast and Competitive Analysis to 2030

Edge Artificial Intelligence Chip  Market Report: Trends, Forecast and Competitive Analysis to 2030

Edge Artificial Intelligence Chip  Trends and Forecast

The future of the global edge artificial intelligence chip market looks promising with opportunities in the consumer device and enterprise device markets. The global edge artificial intelligence chip market is expected to reach an estimated $19.9 billion by 2030 with a CAGR of 7.3% from 2024 to 2030. The major drivers for this market are the growing demand for AI-powered applications at the edge, the rising proliferation of IoT devices and sensors, and the increasing need for data privacy and security.

• Lucintel forecasts that, within the processor category, ASIC is expected to witness the highest growth over the forecast period due to its increasing use for edge computing's inference function in applications relating to artificial intelligence.
• In terms of regions, APAC will remain the largest region over the forecast period due to the rapid growth of the electronics industry in the region.

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Emerging Trends in the Edge Artificial Intelligence Chip  Market

The edge AI chip market is entering a new phase with a rapid transformation driven by several factors that are shaping the technology and application environments. These trends are impacting the existing structure and pushing the development of new features in edge AI chips, enhancing their effectiveness and increasing the diversity of the chips. These developments are significant and should be noted when making projections about market progress and emerging areas that can be capitalized on or developed.

• Integration of AI with Edge Computing: The integration of AI with edge computing is a key focus in the edge AI chip industry. There is an added value when organizations combine edge devices with AI algorithms, enabling them to process data and generate responses in real-time without relying on the cloud. Increased productivity, reduced response times, and enhanced data security are some of the benefits offered by this integration. For example, smart cameras embedded with edge AI chips can process video feeds and provide actionable insights, reducing the need for cloud storage. This aspect fuels the proliferation of autonomous systems, smart cities, and industrial automation applications.
• Advancements in Chip Architecture and Design: Innovations in chip architecture and design are driving the edge AI chip market. Traditional chip designs are being revamped to meet the demands for processing power, energy efficiency, and integration. For example, advancements in neuromorphic chip designs and hardware accelerators are improving the processing of AI algorithms at the edge. As a result, applications such as video analytics and smart robotics are becoming more complex and demanding. The focus on tailoring chip designs for specific AI applications aligns with the quest for efficient and powerful edge devices.
• Greater Emphasis on Energy Efficiency: Energy efficiency is a growing trend in the edge AI chip industry. As more edge devices enter the market, optimizing energy consumption becomes crucial for extending battery life and lowering operational costs. Designers are creating low-power AI chip architectures and high-performance energy management systems. For example, techniques like dynamic voltage and frequency scaling optimization and cooling for low power consumption are being incorporated. This trend supports the use of edge AI chips in battery-powered devices deployed in remote areas, where energy efficiency is especially important.
• Emergence of Edge AI in the Industrial Sector for IoT Usage: The affordable versions of IoT devices, such as small rivet-type devices, have fueled the growth of the edge AI market. Edge AI chipsets in IoT systems enhance local information processing and decision-making. This trend improves the capabilities of smart sensors, connected devices, and industrial IoT systems by providing alert and monitoring capabilities, reducing dependence on cloud services. Predictive maintenance, smart grids, and autonomous inspection are some of the emerging applications of edge AI chips. The demand for intelligent edge solutions and the growth of IoT systems is driving the need for advanced edge AI chips.
• Development of New Industrial and Consumer Applications: A significant trend is the expansion of edge AI chips into new industrial and consumer applications. The adoption of edge AI technology is being seen in industries such as automotive, healthcare, agriculture, and smart home devices. One common use of edge AI chips is in self-driving cars for object detection and real-time navigation. Other examples include healthcare monitoring, where edge AI is used to track patients remotely, and smart homes, where it enables sophisticated control and automation. This trend shows how edge AI chips are becoming more flexible and applicable across various industries, fostering market expansion and innovation.

Trends emerging in the edge AI chip market, such as AI and edge computing convergence, improvements in architecture, increased attention to energy efficiency, the development of edge AI for IoT, and the rise of new applications, are transforming the industry. These trends spur innovation, improve performance, and widen the applicability of edge AI chips, which will shape the future of the market.

Recent Developments in the Edge Artificial Intelligence Chip  Market

Recent developments in the edge AI chip industry reflect the influx of new techniques and the demand for powerful, fast-processing technology. These factors stem from new chip designs, the use of AI, and the need for high-quality edge solutions. Viewing these developments helps track the pulse of the market and identify the parameters favoring growth and improvement.

• Development of Even More Advanced AI Processors: The introduction of high-performance AI chips is a notable achievement in the edge AI space. Major players like NVIDIA and Intel have launched new edge AI chips with better processing and power efficiency. For example, NVIDIA’s Jetson Orin and Intel’s Movidius Myriad X enhance the real-time processing capabilities of edge devices. These chips enable sophisticated solutions in platforms such as self-driving vehicles and industrial robots, thereby growing the market by improving demand and functionality.
• Progress in Energy-Efficient AI Chips: The trend toward energy-efficient AI chips is noticeable in the market today. Manufacturers are focusing on creating chips that consume less power while maintaining optimal performance. Innovative designs, such as low-power architectures and power management techniques, help reduce energy consumption. The use of energy-efficient AI chips is crucial for devices that operate on batteries and in remote conditions, supporting the widespread adoption of edge AI solutions and promoting green technology.
• AI and Edge Computing Solutions: Developments in the edge AI chip market are driven by the synergy between AI and edge computing. This combination enables real-time analytics and control at the network edge, eliminating the need for cloud dependence. Companies are developing chips that integrate edge computing with AI, creating better systems and reducing wait times. This trend supports smart public spaces, machine control in factories, and various types of self-guided machines, all of which contribute to the growing demand for edge intelligence systems.
• Further Development of Applications for AI Chips: One major highlight in the edge AI chip market is the further development of AI chips for novel applications. Edge AI technology is gaining prominence in industries such as healthcare, agriculture (especially smart farming), and consumer electronics. For instance, AI chips are being deployed for telehealth, remote patient monitoring, smart farming, and home automation. This development demonstrates the flexibility of edge AI chips and how they are engineered to suit various markets, leading to increased market expansion and innovation.
• Emergence of New Chip Designs and Architectures: New chip designs and architectures are influencing the edge AI chip market. Performance enhancement, scalability, and flexibility are the primary goals of new chip designs. Neuromorphic computing and hardware accelerators are some breakthroughs that improve the application of AI algorithms in edge devices. These advancements enable more sophisticated and challenging applications, increasing demand for high-end edge AI chips and boosting the market.

Recent changes in the edge AI chip market include the launch of efficient and robust chips, integration with edge computing solutions, expansion into new application areas, and changes in chip designs that are driving innovation and development. These changes highlight the growing market for advanced-edge AI solutions and the factors determining the market growth.

Strategic Growth Opportunities for Edge Artificial Intelligence Chip  Market

The edge AI chip market offers strategic growth opportunities in key sectors of application. As edge computing and AI technologies continue to improve, there are significant opportunities to manufacture and utilize edge AI chips across industries. Identifying and exploiting these opportunities will foster further market growth and the development of new solutions.

• Automotive Sector: The automotive sector offers significant growth potential for edge AI chips. With the rise of autonomous vehicles and advanced driver-assistance systems (ADAS), there is an increasing demand for edge AI chips that can process data from sensors and cameras in real-time. Edge AI chips enhance vehicle safety, navigation, and performance. By investing in automotive-edge AI solutions, companies can capture a significant share of this growing market and contribute to the advancement of autonomous driving technologies.
• Healthcare Applications: Healthcare applications are a fundamental growth area for edge AI chips. Edge AI technology is applied in remote patient monitoring, diagnostics, and personalized treatment. AI chips enable the processing of information at the edge, facilitating timely intervention for patients and enhancing operational efficiency. The growing demand for sophisticated therapeutic devices and home healthcare monitoring systems presents opportunities for companies developing healthcare-edge AI solutions, sustaining market progress, and improving healthcare quality.
• Industrial Automation: The industrial automation sector holds significant growth potential for edge AI chips. These chips can be used in smart factories and manufacturing processes to improve operational efficiency, predictive maintenance, and quality assurance. By enabling local data computation, edge AI chips reduce communication delays and enable real-time responses, enhancing the efficiency and reliability of industrial activities. Companies developing edge AI applications for industrial automation can capitalize on the expanding market for smart manufacturing solutions.
• Smart Cities and Infrastructure: Edge AI chips are well-positioned for growth in the smart cities and infrastructure sector. Solutions for issues like smart traffic management, smart surveillance, and environmental monitoring require edge AI solutions that perform computation locally and provide quick insights. Edge AI chips support the creation of smart infrastructure and improve the quality of life in urban areas. Organizations investing in smart city initiatives will benefit from edge AI technology, which is essential for building efficient, connected cities.
• Consumer Electronics: The consumer electronics sector offers substantial growth opportunities for edge AI chips in devices such as smart home products, wearables, and personal assistants. Edge AI chips enable advanced features like voice recognition, image processing, and personalized user experiences. As consumers increasingly demand functional and smart devices, companies designing and manufacturing consumer electronics with edge AI solutions can expand their market presence and drive innovation in smart technology.

The potential for innovation and market expansion in edge AI chips is evident across sectors such as automotive, healthcare, industrial automation, smart cities, and consumer electronics. These efforts will lead to market progress and the development of edge AI technology that addresses the changing needs of the industry.

Edge Artificial Intelligence Chip  Market Driver and Challenges

The edge AI chip market is influenced by numerous drivers and challenges that significantly impact its growth and evolution. Technological advancements, economic factors, and regulatory aspects shape market dynamics. Analyzing these drivers and challenges helps inform the present and future of the market.

Drivers of the Edge AI Chip Market

• Advancements in AI and Edge Computing Technologies: The rapid development of artificial intelligence algorithms and edge computing infrastructure is a key driver of the market. AI’s ability to improve decision-making at the edge is enhanced by the integration of powerful chips that support AI workloads. This combination boosts the demand for edge AI chips and accelerates market growth.
• Increasing Demand for Real-Time Data Processing: The need for real-time data processing is driving the demand for edge AI chips. The capability to process data locally reduces the dependency on cloud computing and enables faster decision-making. Edge AI chips are crucial for industries like autonomous vehicles, manufacturing, and healthcare, where real-time processing is essential.
• Low Latency and High Bandwidth Needs: Industries that rely on time-sensitive applications, such as autonomous vehicles and industrial automation, demand low latency and high bandwidth for efficient data processing. Edge AI chips, which provide local computation, address these needs by reducing delays and ensuring smooth data flows

Challenges of the Edge AI Chip Market

• Complexity of Design and Integration: The design and integration of edge AI chips require advanced engineering and expertise. Balancing processing power, energy efficiency, and size is a complex challenge, as chips need to meet specific use cases while maintaining low power consumption. This complexity can increase development time and costs.
• High Power Consumption in Some Applications: While energy-efficient chips are being developed, some edge AI applications, particularly in high-performance scenarios, require considerable power. The balance between performance and power efficiency remains a challenge for designers, especially in battery-powered devices.
• Regulatory and Privacy Issues: Regulatory challenges, including data privacy and security concerns, pose obstacles to the widespread adoption of edge AI chips. Edge devices often handle sensitive data, and ensuring compliance with global data protection laws, such as GDPR, can be a challenge for manufacturers.

The edge AI chip market is shaped by a mix of technological advancements, real-time data processing demands, and challenges related to integration, power consumption, and regulatory concerns. Understanding these drivers and challenges is crucial for navigating the market’s current state and future potential.

List of Edge Artificial Intelligence Chip  Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leverage integration opportunities across the value chain. With these strategies edge artificial intelligence chip  companies cater increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the edge artificial intelligence chip  companies profiled in this report include-

• Advanced Micro Devices
• Alphabet
• Intel
• Qualcomm Technologies
• Apple
• Mythic
• Arm

Edge Artificial Intelligence Chip  by Segment

The study includes a forecast for the global edge artificial intelligence chip  by processor, function, product type, and region.

Edge Artificial Intelligence Chip Market  by Processor [Analysis by Value from 2018 to 2030]:

• CPU (Central Processing Unit)
• GPU (Graphics Processing Unit)
• ASIC (Application-Specific Integrated Circuit)
• Others

Edge Artificial Intelligence Chip Market  by Function [Analysis by Value from 2018 to 2030]:

• Training
• Inference

Edge Artificial Intelligence Chip Market  by Product Type [Analysis by Value from 2018 to 2030]:

• Consumer Devices
• Enterprise Devices

Edge Artificial Intelligence Chip Market  by Region [Shipment Analysis by Value from 2018 to 2030]:

• North America
• Europe
• Asia Pacific
• The Rest of the World

Country Wise Outlook for the Edge Artificial Intelligence Chip  Market

It has been observed that due to the progress of such technologies and the need for processing at the edge in real-time, the edge AI chip industry has experienced revolutionary changes. Rather than requiring a connection with the cloud, edge AI chips are embedded within the devices and calculations take place within the devices themselves. This development is useful for automotive, industrial, healthcare, consumer, and electronics applications. Recent advances reflect changes in the chip architectures, mounting demands for the integration of AI into chips, and the quest for greater efficiency and effectiveness. Such progress is driven by broadening horizons of smart devices, autonomous systems, internet-enabled solutions, and, more importantly, the relevance of such devices in the ecosystems of modern technology.

• United States: Edge AI chips in the US have seen notable progress concerning the chip architecture and the other associated advancements. A notable improvement of the processors initiative has been undertaken by the likes of NVIDIA and Intel in the development of new edge AI chips. Cutting-edge chips for instance NVIDIA’s Jetson Orin platform and the Intel Movidius Myriad X have been specifically built for edge devices to perform AI processes in real-time. There is a growing market for these chips in industries such as autonomous vehicles, smart cities, and industrial automation. Furthermore, there is a growing trend of ensuring that AI and edge computing are used in tandem in a bid to enhance data processing and reduce latency.
• China: China, on the other hand, is not left behind in the search for the edge AI chip, with the backing of the tech industry and more specifically the likes of Huawei and Alibaba. Applications of high-performance edge AI chips designed for smart security to industrial IoT can be found in Huawei’s Ascend 310, as well as in Alibaba’s Hanguang 800. The development of AI in China is being stimulated by government patronage as well as now the transition to being technology-independent. Enhancement of the energy efficiency and cost-efficiency of the edge AI chips is also being looked at by Chinese firms to ensure the masses and the consumer electronics and smart infrastructure can embrace it easily.
• Germany: Germany’s edge AI chip market characteristics are its focus on accuracy and effectiveness in line with the country’s capabilities in engineering and manufacturing. Infineon and Bosch are at the forefront of the new developments making new AI chips for automotive and industrial applications. The AURIX™ TC4x series by Infineon and Bosch’s new edge AI chips are meant for autonomous driving and smart manufacturing. Moreover, the German Dedication to Industry and smart factories is also a reason that has come up with edge AI chips that help in the performance of processes and provide data analysis in real-time.
• India: In India, the edge AI chip market is still presenting itself with affordable and scalable options whenever applicable. Startups and companies are developing edge AI chips for various applications especially cities and urban applications. For instance, Arya.ai and Niramai companies target edge AI chips to provide solutions in healthcare and agritech. Also, interaction with major high-edge AI companies is becoming more common and easier as they are bringing advanced edge AI controls to India. The aim is mainly to produce cheap and performance-efficient chips that would be useful in supporting the ever-growing digital and industrial landscape in the country.
• Japan: The edge AI chip market in Japan has experienced growth driven by many factors such as the development of semiconductor technology and the incorporation of robotics and automation. Leading companies like Sony and Renesas are actively engaging in the creation of edge AI chips for consumer electronic products and industrial automation. The goal of Sony’s newly developed edge AI chips is to improve the image and video processing platform in consumer electronics whereas Renesas is working on chips designed specifically for the automotive and industrial sectors. Due to the ever-strong quest in Japan for original ideas in product creation and quality manufacturing, there are also advancements of more advanced edge AI chips supporting various applications such as robotics, smart homes, and industrial IoTs.

Features of the Global Edge Artificial Intelligence Chip Market 

Market Size Estimates: Edge artificial intelligence chip market  size estimation in terms of value ($B).

Trend and Forecast Analysis: Market trends (2018 to 2023) and forecast (2024 to 2030) by various segments and regions.

Segmentation Analysis: Edge artificial intelligence chip market  size by processor, function, product type, and region in terms of value ($B).

Regional Analysis: Edge artificial intelligence chip market  breakdown by North America, Europe, Asia Pacific, and Rest of the World.

Growth Opportunities: Analysis of growth opportunities in different processors, functions, product types, and regions for the edge artificial intelligence chip market .

Strategic Analysis: This includes M&A, new product development, and competitive landscape of the edge artificial intelligence chip market .

Analysis of competitive intensity of the industry based on Porter’s Five Forces model.

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This report answers following 11 key questions:

Q.1. What are some of the most promising, high-growth opportunities for the edge artificial intelligence chip market  by processor (CPU, GPU, ASIC, and others), function (training and inference), product type (consumer devices and enterprise devices), and region (North America, Europe, Asia Pacific, and the Rest of the World)?

Q.2. Which segments will grow at a faster pace and why?

Q.3. Which region will grow at a faster pace and why?

Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?

Q.5. What are the business risks and competitive threats in this market?

Q.6. What are the emerging trends in this market and the reasons behind them?

Q.7. What are some of the changing demands of customers in the market?

Q.8. What are the new developments in the market? Which companies are leading these developments?

Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?

Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?

Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

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