Global Edge AI Chips Market By Processor (CPU, GPU, ASIC, Others), By Device Type (Consumer Devices, Enterprise Devices), By Function (Training, Inference), Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends and Forecast 2024-2033

Report Overview

The Global Edge AI Chips Market size is expected to be worth around USD 25.2 Billion By 2033, from USD 2.4 Billion in 2023, growing at a CAGR of 26.5% during the forecast period from 2024 to 2033.

Edge AI chips, also known as AI processors or AI accelerators, are specialized hardware components designed to perform artificial intelligence computations directly on edge devices. These chips enable on-device AI processing, allowing edge devices such as smartphones, IoT devices, and autonomous vehicles to perform AI tasks efficiently and in real-time.

The market for Edge AI chips is experiencing significant growth due to various factors. Edge AI refers to the deployment of artificial intelligence algorithms and processes directly on edge devices, such as smartphones, Internet of Things (IoT) devices, and autonomous vehicles, rather than relying on cloud-based computing. This approach offers several advantages, including reduced latency, enhanced privacy, and improved efficiency.

One of the key growth factors driving the Edge AI chips market is the increasing demand for real-time and low-latency processing. Edge devices require rapid decision-making capabilities to enable applications such as autonomous driving, smart surveillance, and industrial automation. By leveraging AI algorithms on dedicated chips, the processing can be performed locally, minimizing latency and enabling faster response times.

Another factor contributing to the growth of the Edge AI chips market is the proliferation of IoT devices. With the rise of connected devices, there is a need to process and analyze data at the edge to reduce bandwidth usage and ensure timely insights. Edge AI chips enable on-device processing, allowing IoT devices to perform tasks such as data filtering, pattern recognition, and predictive analytics without relying solely on cloud-based resources.

Despite the growth opportunities, there are challenges in the Edge AI chips market. One such challenge is the need for efficient power consumption. Edge devices often operate on limited power sources, such as batteries, and energy-efficient AI chips are crucial to ensure optimal performance without draining the device’s power supply.

Additionally, there is a need for standardized architectures and programming frameworks to facilitate the development and deployment of AI applications on edge devices. Streamlining the development process and ensuring compatibility across different chip manufacturers can accelerate the adoption of Edge AI technology.

However, the Edge AI chips market offers significant opportunities for innovation and market expansion. As AI algorithms become more complex and demanding, there is a growing need for specialized chips that can handle the computational requirements efficiently. The development of high-performance Edge AI chips with improved power efficiency and enhanced capabilities can unlock new possibilities for a wide range of industries.

Analyst Viewpoint

According to Market.us, The Global Edge AI Market is expected to experience tremendous growth in the coming years. According to industry analysts, the market size is predicted to reach around USD 143.6 Billion by 2033, up from USD 19.1 Billion in 2023, reflecting a robust Compound Annual Growth Rate (CAGR) of 25.9% during the forecast period from 2024 to 2033.

Similarly, the Edge AI Hardware Market is also poised for substantial expansion. The market size is expected to grow from USD 8 Billion in 2023 to approximately USD 43 Billion by 2033, a CAGR of 19.2% over the same period.

Moreover, the AI Chip Market as a whole is projected to witness even more rapid growth. Analysts estimate the market size will reach around USD 341 Billion by 2033, up from USD 23.0 Billion in 2023, representing a remarkable CAGR of 31.2% during the forecast period.

To provide further context, in 2020, approximately 750 million Edge AI chips were sold, generating around USD 2.6 Billion in revenue. This figure is more than twice the 300 million edge AI chips Deloitte predicted would sell in 2017, underscoring the accelerating adoption of this technology.

Looking ahead, industry experts anticipate that by 2024, sales of edge AI chips will exceed 1.5 billion units, representing an annual unit sales growth of at least 20%, more than double the longer-term forecast of 9 percent CAGR for the overall semiconductor industry. Furthermore, the impact of edge AI is expected to be significant. Edge intelligence devices are estimated to handle 18.2 zettabytes of data per minute by 2025, and edge AI is expected to reduce cloud traffic by up to 99% by the same year.

The market’s growth potential is further highlighted by recent industry developments, such as Microsoft’s launch of Azure Percept, its new platform for edge AI and machine learning, in March 2021. Additionally, a recent Omdia report states that edge AI processor revenue will rise from $31 billion in 2022 to $60 billion in 2028, underscoring the tremendous opportunities in this rapidly evolving market.

Key Takeaways

• The Edge AI Chips Market size is estimated to reach USD 25.2 billion in the year 2033 with a CAGR of 26.5% during the forecast period and was valued at USD 2.4 billion in the year 2023.
• In 2023, the CPU segment held a dominant market position, capturing more than a 36.7% share of the Edge AI Chips market.
• In 2023, the consumer device segment held a dominant market position, capturing more than a 64.5% share of the Edge AI Chips market.
• In 2023, the inference segment held a dominant market position, capturing more than a 56.5% share of the Edge AI Chips market.
• In 2023, North America held a dominant market position in the Edge AI Chips market, capturing more than a 42.3% share, with revenues amounting to USD 1.01 billion.

Processor Analysis

In 2023, the CPU segment held a dominant position in the Edge AI Chips market, capturing more than a 36.7% share. CPUs, or Central Processing Units, are fundamental to the architecture of traditional computing systems and continue to play a pivotal role in Edge AI applications.

This predominance is largely attributed to their versatility and efficiency in handling a variety of algorithms and tasks. CPUs are well-suited for sequential task processing, which is essential for many AI applications that require complex decision-making processes based on a series of steps. Moreover, CPUs offer a high degree of compatibility with existing software and programming frameworks, making them an attractive option for developers who seek to integrate AI capabilities into a broad range of products without significant retooling.

The established development ecosystem around CPUs allows for rapid deployment and scaling of AI functionalities across multiple industries, including automotive, healthcare, and manufacturing. Their ability to efficiently manage diverse workloads and support the integration of AI at the edge, where real-time data processing is crucial, further explains their leading position in the market. Additionally, ongoing advancements in CPU technology, such as increased processing power, energy efficiency, and integrated AI-specific enhancements, continue to bolster their applicability in edge computing scenarios.

Manufacturers are progressively optimizing CPUs to better handle AI workloads by enhancing their instruction sets and integrating capabilities such as simultaneous multithreading, which significantly improves performance in AI-driven tasks. This evolution ensures that CPUs remain a compelling choice for developers and businesses aiming to leverage Edge AI to enhance operational efficiency and data-driven decision-making.

Device Type Analysis

In 2023, the Consumer Devices segment held a dominant market position in the Edge AI Chips market, capturing more than a 64.5% share. This leadership stems primarily from the expansive growth in smart home devices, wearables, and mobile technologies, where Edge AI chips play a crucial role. These chips enhance user experiences by enabling faster processing at the device level, reducing latency, and preserving privacy by minimizing the need to send data back to central servers.

Consumer devices increasingly incorporate AI capabilities to provide more personalized and responsive functionalities. From smartphones that offer real-time voice translation to smart watches capable of monitoring health metrics with improved accuracy, Edge AI chips are integral to these innovations. The proliferation of these devices in everyday use drives demand for chips that can process data locally, leading to significant growth in this segment.

Furthermore, the push towards more intelligent consumer electronics that can operate with minimal connectivity issues and lower power consumption also favors the adoption of Edge AI chips. As manufacturers continue to integrate AI into a wider array of products, from cameras to personal assistants, the consumer devices segment is expected to maintain its lead, powered by consumer demand for smarter, more autonomous technology that enhances daily life.

Function Analysis

In 2023, the Inference segment held a dominant market position in the Edge AI Chips market, capturing more than a 56.5% share. This segment’s prominence is attributed to the critical role that inference plays in the deployment of AI applications within edge devices. Inference involves using a trained AI model to make decisions and predictions based on new data. Because these processes must happen quickly and locally to ensure real-time responsiveness without latency issues, edge AI chips designed for inference are essential.

The widespread adoption of edge computing in industries such as automotive for advanced driver-assistance systems (ADAS), in retail for customer behavior tracking, and in healthcare for real-time patient monitoring systems underscores the need for robust inference capabilities. These applications require immediate processing at the source of data collection to function effectively, making inference-focused AI chips invaluable.

The ability to process data locally without relying on cloud connectivity ensures that systems are not only faster but also more reliable and secure, as sensitive data does not need to be transmitted over networks. Moreover, the demand for inference chips is bolstered by the growth of IoT devices and the expansion of smart city infrastructure, where instantaneous data processing is crucial for operational efficiency and safety.

These chips are optimized for power efficiency and rapid data processing, making them ideal for deployment in environments where power and data connectivity are limited. As technology progresses and more devices become capable of sophisticated AI tasks at the edge, the inference segment is projected to continue its growth trajectory, driven by the need for high-performance, real-time AI processing at the edge of networks.

Key Market Segments

By Processor

• CPU
• GPU
• ASIC
• Others

By Device Type

• Consumer Devices
• Enterprise Devices

By Function

• Training
• Inference

Driver

Rising Demand in IoT Applications

The Edge AI Chips market is primarily driven by the escalating integration of Internet of Things (IoT) technologies across various sectors such as industrial automation, smart homes, and healthcare. The deployment of IoT devices, which often incorporate AI capabilities for data processing at the edge, is increasing rapidly. This surge is linked to the benefits of real-time analytics and localized data processing, reducing latency and enhancing operational efficiency, as seen in applications ranging from predictive maintenance in industries to real-time health monitoring in wearable tech

Restraint

Complexity in Design and Integration

A significant restraint in the Edge AI Chips market is the complexity associated with designing and integrating these chips into existing systems. As edge computing requires sophisticated AI algorithms to process data locally, the design of these chips often involves intricate architectural considerations to balance power efficiency with high performance. This complexity can increase the time and cost of deploying edge AI solutions, potentially slowing down adoption rates among industries that are less technologically advanced​

Opportunity

Advancements in AI and Machine Learning

The Edge AI Chips market is poised to capitalize on advancements in artificial intelligence and machine learning technologies. With continuous improvements in AI algorithms and the growing proficiency of neural networks, edge devices are becoming more capable of handling complex AI tasks independently. This trend is likely to expand the market’s scope, encompassing applications like advanced driver-assistance systems (ADAS), autonomous drones, and smart city technologies.

Furthermore, the integration of AI into consumer electronics for enhanced user interfaces and automation presents substantial growth opportunities. The development of AI-driven personal assistants and smarter home automation systems exemplifies how AI advancements can drive market expansion

Challenges

Security Concerns in Edge Computing

A prominent challenge facing the Edge AI Chips market revolves around cybersecurity risks associated with edge computing. The dispersion of processing activities across numerous edge devices broadens the attack surface for potential cyber threats. Ensuring the security of data being processed at the edge, particularly in critical applications like healthcare and finance, remains a significant hurdle.

Organizations need to invest in robust security measures to protect against vulnerabilities that could be exploited in decentralized AI environments. This includes the development of advanced encryption technologies and secure data protocols to safeguard sensitive information processed outside traditional data centers​

Growth Factors

• The proliferation of IoT Devices: The exponential increase in IoT devices across various sectors drives the need for real-time data processing capabilities provided by edge AI chips.
• Advancements in Semiconductor Technology: Continuous improvements in semiconductor technology enhance the performance and efficiency of edge AI chips, making them more viable for widespread deployment.
• Demand for Real-Time Data Processing: Applications requiring low-latency and high-efficiency computation, such as autonomous vehicles, smart cities, and industrial automation, boost the demand for edge AI chips.
• Focus on Data Privacy and Security: Processing data locally on edge devices reduces the risk of data breaches and ensures compliance with stringent regulatory requirements, promoting the adoption of edge AI chips.
• Growth of Autonomous Vehicles: The increasing development and deployment of autonomous vehicles necessitate robust, real-time data processing capabilities provided by edge AI chips.
• Expansion of Smart Home Ecosystems: The rising popularity of smart home devices that rely on AI for enhanced functionality drives the demand for edge AI chips.
• Decreasing Costs of Edge AI Chips: As the technology matures, the costs associated with edge AI chips decrease, making them more accessible and driving their adoption across various industries.

Latest Trends

• Rapid Adoption Across Industries: Edge AI chips are increasingly being integrated into various sectors such as automotive, healthcare, retail, and manufacturing to enable real-time data processing and decision-making at the edge of networks.
• Miniaturization and Power Efficiency: There’s a growing emphasis on developing smaller, more power-efficient edge AI chips to meet the stringent size and energy requirements of edge devices and IoT sensors.
• Customization for Specific Applications: Manufacturers are focusing on offering customizable edge AI chip solutions tailored to specific industry needs, optimizing performance for diverse applications ranging from autonomous vehicles to medical diagnostics.
• Enhanced Processing Capabilities: Continuous advancements in AI algorithms and chip architectures are enabling edge devices to handle increasingly complex tasks locally, reducing dependency on centralized cloud resources and enhancing responsiveness.
• Security and Privacy Enhancements: Integration of robust security features within edge AI chips to mitigate data breaches and ensure privacy compliance, addressing concerns related to data sovereignty and regulatory requirements.
• Edge-to-Cloud Synergy: Establishing synergies between edge computing and cloud platforms to leverage the strengths of both paradigms, facilitating seamless data flow and scalable AI applications across distributed environments.

Regional Analysis

In 2023, North America held a dominant market position in the Edge AI Chips market, capturing more than a 42.3% share, with revenues amounting to USD 1.01 billion. This leadership can be attributed to several factors, including the rapid adoption of advanced technologies and the strong presence of leading tech companies that drive innovation in AI and machine learning.

North America, particularly the United States, is a hub for technological advancement with substantial investments in AI research and development, facilitating the early adoption of edge computing technologies across various sectors such as healthcare, automotive, and manufacturing.

Moreover, the region benefits from a well-established infrastructure supporting IoT and AI, coupled with governmental policies that encourage innovation in AI technologies. For example, initiatives aimed at enhancing data privacy and security have made it easier for industries to adopt edge AI solutions without compromising sensitive information. Additionally, the growing demand for real-time data processing and analysis in industries such as retail, finance, and telecommunications further accelerates the deployment of edge AI chips in this region.

The push towards autonomous vehicles, smart cities, and digital healthcare in North America also plays a crucial role in expanding the market for Edge AI chips. These applications rely heavily on real-time, efficient processing of large volumes of data at the edge, driving demand for sophisticated AI chips capable of supporting such advanced functionalities.

Key Regions and Countries

• North America
  • US
  • Canada
• Europe
  • Germany
  • France
  • The UK
  • Spain
  • Italy
  • Russia
  • Netherlands
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • South Korea
  • India
  • Australia
  • Singapore
  • Thailand
  • Vietnam
  • Rest of APAC
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • South Africa
  • Saudi Arabia
  • UAE
  • Rest of MEA

Key Players Analysis

The Edge AI Chips Market is experiencing significant growth, driven by the advancements in AI and the increasing demand for real-time data processing. Advanced Micro Devices, Inc. is a prominent player, known for its high-performance processors that support AI workloads at the edge.

Alphabet Inc., through its Google division, is investing heavily in edge AI, leveraging its Tensor Processing Units (TPUs) for efficient AI computations. Intel Corporation continues to dominate with its versatile AI chips that cater to various edge applications, emphasizing low power consumption and high performance.

Qualcomm Technologies, Inc. is renowned for its AI solutions integrated into mobile and IoT devices, enhancing edge processing capabilities. Apple Inc. has made strides with its Neural Engine, designed to accelerate AI tasks on its devices, providing a seamless user experience. Mythic Ltd. offers innovative AI inference solutions that deliver high performance with low power consumption, making it a key player in the market. Arm Limited is influential in the edge AI landscape, providing architecture that supports a wide range of AI applications.

Top Key Players in the Market

• Advanced Micro Devices, Inc.
• Alphabet Inc.
• Intel Corporation
• Qualcomm Technologies, Inc.
• Apple Inc.
• Mythic Ltd.
• Arm Limited
• Samsung Electronics Co., Ltd.
• NVIDIA Corporation
• Xilinx Inc.
• Other key players

Recent Developments

• January 2024: AMD announced the Versal™ AI Edge XA adaptive SoC and Ryzen™ Embedded V2000A Series processors at CES 2024. These products are designed for advanced automotive systems, including forward cameras, in-cabin monitoring, LiDAR, and autonomous driving applications.
• February 2024: Google announced the Tensor Processing Unit (TPU) v5 at their annual AI Summit. The new TPU focuses on edge AI applications, offering enhanced performance for machine learning tasks in distributed environments.
• March 2024: Intel released the Movidius Myriad X2 VPU, a vision processing unit designed for edge AI workloads. This product aims to provide advanced computer vision and deep learning capabilities in compact devices.
• In June 2023: NVIDIA introduced the Jetson Orin Nano, a new module designed for edge AI and robotics applications, providing powerful AI performance in a compact form factor.

Report Scope