Edge AI market to reach $25.2 billion by 2033

Entry

According to Market.us, the Edge AI chipset market is poised for significant growth, with growth expected to reach $25.2 billion by 2033, developing at a rapid pace CAGR 26.5% throughout 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 AI computations directly on edge devices. This capability enables devices such as smartphones, IoT gadgets, and autonomous vehicles to quickly perform AI tasks in real time, without heavy reliance on cloud computing.

The edge AI market is witnessing significant growth, driven by the growing demand for faster processing and real-time decision-making in devices at the edge of the network. These chips are designed to process data locally, reducing latency and increasing performance for applications such as autonomous vehicles, smart home devices, and industrial automation. The rapid expansion of IoT devices and advancements in machine learning algorithms are further driving the growth of this market.

However, the market faces challenges such as high development costs and the complexity of integrating AI capabilities into small, energy-efficient chips. Additionally, there are concerns about data security and privacy, as edge devices often process sensitive information. The opportunities in this market are numerous, especially in emerging areas such as healthcare for remote monitoring and diagnostics and smart cities for traffic management and public safety. Ongoing innovation in semiconductor technologies also creates opportunities for market participants to develop more efficient and energy-efficient AI edge chips that meet the changing needs of various industries.

Key conclusions

• This Edge AI Market It is expected to reach $25.2 billion By 2033is growing at a pace CAGR 26.5% in the forecast period. In 2023the market was valued at $2.4 billion.
• IN 2023this Processor Segment dominated the market, gaining over 36.7% Participation in Edge AI Market.
• This consumer devices segment He also held a significant position in 2023which is more than 64.5% market share.
• Additionally, inference segment led the market in 2023with a share exceeding 56.5%.
• Geographically, North America was the leading region in Edge AI Market IN 2023having more than 42.3% share, with revenues amounting to $1.01 billion.

Edge AI Chip Statistics

• Edge AI Market It is expected to reach approximately USD 143.6 billion by 2033up from $19.1 billion in 2023which reflects the compound annual growth rate (CAGR) 25.9% in the forecast period from 2024 to 2033
• Edge AI Hardware Market Its value is expected to be approximately $43 billion by 2033rising from $8 billion in 2023with a compound annual growth rate of CAGR of 19.2% in the forecast period from 2024 to 2033
• Global AI Integrated Circuit Market It is expected to reach a valuation of around $341 billion by 2033grows with USD 23.0 billion in 2023with a compound annual growth rate of CAGR of 31.2% in the forecast period from 2024 to 2033
• From 2020 approximately 750 million Edge AI chips were sold, generating approximately $2.6 billion in revenueThis growth trajectory indicates that Edge AI chips are increasingly being integrated into a variety of consumer IoT devices, including advanced smartphones, smart speakers, wearables, tablets, and smart video doorbells.
• This $2.6 billion revenues significantly exceed 300 million Edge AI chips Deloitte forecasts sales for 2017
• Edge Intelligence Devices it is expected that they will be processed 18.2 zettabytes of data per minute by 2025.
• Edge AI IC sales are expected to exceed 2024 1.5 billion unitspotentially exceeding that number by a significant margin.
• This forecast means annual unit sales growth of at least 20%which is more than twice the long-term forecast 9% CAGR for the entire semiconductor industry.
• In March 2021 Microsoft shot Azure Percepta new platform dedicated to artificial intelligence and machine learning.
• Edge AI is expected to reduce cloud traffic by up to 99% by 2025.
• Some Omdia The report indicates that revenues from AI processors at the edge of the network will increase from USD 31 billion in 2022 Down $60 billion in 2028.

New trends

• Increased adoption in consumer electronics:Demand for edge AI chips in consumer electronics, especially smartphones, is growing rapidly. This is due to the improved capabilities these chips provide in processing AI tasks directly on devices, improving performance and efficiency.
• Growth in IoT devices:There has been a significant increase in the adoption of Edge AI chips in IoT devices. This trend is driven by the need to process data in real time and make decisions at the device level, without relying on cloud connectivity.
• Advances in Integrated Circuit Technology: New advances in semiconductor technology are enabling more efficient and energy-efficient edge AI chips. These advances are key because they enable broader applications across industries, including automotive and industrial automation.
• Integration with 5G technology:The deployment of 5G networks enhances the capabilities of edge AI by reducing latency and increasing the speed and reliability of data transmission. This integration is crucial for applications requiring real-time analysis and processing.
• Focus on Sustainability:There is an increasing focus on designing Edge AI chips that are not only efficient but also energy-efficient. This shift is a response to growing environmental concerns and the need for sustainable technology solutions across industries.

Top Edge AI Use Cases

• Smartphones and wearables:Edge AI chips are widely used in smartphones and wearables to process data on-device to enable functions such as facial recognition, augmented reality, and real-time language translation, improving user experience without requiring a constant connection to the cloud.
• Automotive applications:In the automotive sector, artificial intelligence (AI) chips are used in advanced driver assistance systems (ADAS) to process information in real time, which is critical for safety features such as object detection and collision avoidance.
• Industrial automation:These chips are also transforming industrial automation by making it easier to analyze and respond to operational data in real time, increasing efficiency and reducing downtime through predictive maintenance and anomaly detection.
• Medical equipment:In healthcare, AI-based chips are powering device monitoring and diagnostics by locally processing complex data sets to provide immediate insights, which is essential for monitoring patients and responding to emergencies.
• Smart home technology:Edge AI chips enable smart home devices like security cameras and voice assistants to process data on-device, improving responsiveness and privacy by reducing the amount of data sent to the cloud.

Bigger challenges

• Market saturation:Some segments, such as the smartphone market, are approaching saturation with existing AI architectures integrated across multiple chipsets, which could limit new market entry or expansion.
• High development costs:The advanced technology required to produce Edge AI chips involves significant research and development costs, including costs associated with advanced semiconductor packaging and manufacturing technologies.
• Technological complexity:The complexity of building Edge AI solutions that are efficient, reliable, and capable of running advanced AI algorithms at the edge of the network is a significant challenge.
• Regulatory and safety concerns:Because Edge AI chips process data locally, they must comply with different regional data protection regulations, making their deployment in different markets difficult.
• Supply chain issues:Global supply chain disruptions could impact the production and delivery of Edge AI chips, negatively impacting overall market growth and stability.

Edge AI Market Opportunities

• Implementation of 5G technology:The deployment of 5G networks enables high-speed, low-latency communications that can empower Edge AI applications, especially in real-time data processing and analytics.
• Growth in IoT and smart devices:The increasing adoption of IoT and smart devices across sectors including automotive, healthcare and consumer electronics is driving demand for Edge AI chips that enable efficient data processing at the source.
• Advances in Artificial Intelligence and Machine Learning:Continued improvements in AI and machine learning algorithms are expanding the capabilities of Edge AI systems, making them more attractive for new applications such as autonomous vehicles and smart manufacturing.
• Energy efficiency needs:As energy efficiency becomes increasingly important, Edge AI chips that minimize data transmission to the cloud and reduce power consumption offer significant benefits and market opportunities.
• Various areas of application:Edge AI chips have a wide range of applications, from video surveillance and security to advanced driver assistance systems (ADAS), providing multiple paths to market expansion.

Last changes

• April 2024:Intel has introduced next-generation Xeon processors with integrated AI accelerators aimed at boosting AI performance in edge computing environments.
• March 2023:Google introduced the Coral Dev Board Micro, a compact development board for AI applications at the edge, powered by the Google Edge TPU to accelerate machine learning workloads at the edge.
• February 2024:Qualcomm introduced the Snapdragon X75 5G modem, which offers advanced AI capabilities for edge devices, boosting connectivity and AI processing at the edge of the network.
• October 2023:Samsung has launched the Exynos 2200 chipset equipped with an integrated AI engine that aims to improve AI processing capabilities in smartphones and edge devices.

Application

The Edge AI market is at the intersection of several powerful technology trends, including the growth of IoT, 5G deployment, and advances in machine learning. While the market faces challenges such as technological complexity and supply chain vulnerabilities, evolving applications and the shift to more localized real-time processing present significant growth opportunities. As these technologies evolve, demand for Edge AI is expected to increase, especially in applications requiring fast processing at the edge of the network. The market potential is underscored by the integration of Edge AI across industries, driving innovation and efficiency across sectors.