AI adoption on edge devices will boom through the consumer electronics, automotive, and manufacturing industries.
IDTechEx’s market research projects that the global AI chips market for edge devices will grow to $22 billion by 2034, with the three largest industry verticals at that time being consumer electronics, industrial, and automotive.
The projected growth is uneven due to saturation and intermittent progress in markets that already use AI architectures in their chipsets. For instance, the smartphone market is saturated despite the ongoing trend of selling more premium smartphones with AI capabilities. This saturation is expected to continue over the next ten years.
The report highlights two notable spikes in revenue from 2024-25 and 2026-27. The first is mainly attributed to Advanced Driver-Assistance Systems (ADAS) being incorporated into car manufacturers' 2025 production lines.
The second spike is influenced by the increased adoption of ADAS systems and the maturation of start-ups focusing on embedded devices, particularly for smart home appliances. The growth of the consumer electronics industry is a significant opportunity for AI in homes, as it’s expected to generate the highest revenue for edge AI chips in 2034.
AI and Wi-Fi connectivity are two key technologies for realizing an Internet of Things (IoT), where appliances can communicate directly with one another. Smart televisions, mirrors, virtual reality headsets, sensors, kitchen appliances, cleaning appliances, and safety systems can all be interconnected.
AI chips crucial for IoT development
Edge AI chips process data at the network’s edge, which means they can work on a device without any connection to the cloud. Edge devices include cars, cameras, laptops, mobile phones, autonomous vehicles, etc. In all of these instances, computation is carried out close to the user, at the edge of the network where the data is located.
The chips also process data directly on devices, eliminating the need for a cloud connection. They are smaller, more efficient, affordable, and suitable for handheld devices like smartphones, as well as integration into robots and IoT devices compared to their predecessors.
With local AI computations, Edge AI chips reduce the reliance on transferring data to a remote server, enhancing efficiency and mitigating the risk of data interception during transit. Additionally, they filter out irrelevant data, decreasing the chances of transmitting sensitive but unimportant information.
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