A recent report by Berg Insight reveals that the global on-device AI market reached US$ 10.1 billion in 2024, marking a significant increase of approximately 22% from the previous year.
This growth encompasses various components such as AI system-on-chips (SoCs), system-on-modules (SoMs), AI accelerators, and specialised software and platforms designed for on-device AI applications.
However, the figures do not include revenue from non-IoT applications like smartphones and tablets. Projections indicate that the market could expand to US$ 30.6 billion by 2029, with a compound annual growth rate (CAGR) of 25%.
“Over the past decade, the on-device AI market has been driven primarily by traditional machine learning use cases such as computer vision and anomaly detection, which have seen steady annual growth of around the 10 percent range,” said Berg Insight IoT analyst Melvin Sorum.
He highlighted that the landscape has transformed recently, as emerging technologies like generative AI, robotics, and autonomous driving introduce new growth opportunities.
The on-device AI market is characterised by a diverse range of technologies and applications, distinguishing it from cloud-based AI, which typically relies on predefined use cases integrated into centralised systems. In contrast, embedded AI processing can be tailored to various end use cases, allowing for integration into a wide spectrum of devices across consumer, industrial, and automotive sectors.
This variability leads to a differentiated market, presenting unique design constraints and performance requirements.
Berg Insight has identified 40 key players within this sector, which can be broadly categorised into two layers. The first layer includes hardware types such as AI SoCs, SoMs, AI accelerators, and microcontroller units (MCUs), each designed for specific performance, power efficiency, and integration needs. AI SoCs combine various computing cores, on-chip memory, and connectivity into a single chip, while SoMs expand on this by including additional system memory and storage.
The second layer consists of on-device AI platforms that integrate hardware, software, and developer tools, aimed at simplifying the deployment and optimisation of AI models. These developments reflect the market's ongoing evolution and highlight the increasing importance of on-device AI capabilities in an array of applications.
