According to the latest industry report from Research and Markets, the global edge computing market is expected to reach $65 billion in 2026, maintaining a compound annual growth rate of over 20%. Edge AI, as the core growth engine of edge computing, is accelerating its penetration into smart manufacturing, smart cities, autonomous driving, and healthcare, driving sustained surges in demand for embedded processors, edge gateways, and related modules.
The report identifies three primary drivers behind the rapid growth of the edge computing market. First, rising data privacy and compliance requirements are pushing enterprises to process data locally. Second, the proliferation of 5G networks has reduced communication latency between edge nodes and the cloud. Third, advances in AI model lightweighting technologies have enabled large models that previously could only run in the cloud to be deployed on resource-constrained embedded devices. Together, these three trends are driving the transformation of Edge AI from an “optional solution” into “infrastructure.”
At the hardware level, embedded processors are undergoing an architectural shift from “general-purpose computing” to “AI-accelerated computing.” Mid-to-high-end AIoT chips such as Rockchip’s RK3588 and RK3576 already integrate 6 to 8 TOPS of NPU compute, supporting multi-channel video codec and real-time AI inference. In the high-end market, platforms such as NVIDIA Jetson and Qualcomm RB series offer tens to hundreds of TOPS of compute to meet more complex edge AI task requirements.
For embedded core board manufacturers, the explosion of the Edge AI market means product definitions need to be upgraded in tandem. Traditional “CPU plus memory plus storage” core boards can no longer meet customer needs. A new generation of AI core boards integrating NPUs, supporting multi-camera inputs, and featuring industrial-grade temperature ranges is becoming the market mainstream. Junyi Microelectronics will continue to invest in Edge AI core board R&D, providing customers with high-compute, low-power, long-supply-cycle embedded solutions based on RK3588/RK3576 platforms.
Source: Research and Markets