Google AI defines embedded AI as "the deep fusion of artificial intelligence with physical devices and systems". Through this convergence, devices no longer have to rely on cloud connectivity and can process data, analyze intelligence, make decisions and even execute actions locally.
To achieve these capabilities, "lean" AI models need to be run on highly efficient hardware to improve computational efficiency, accuracy and real-time response. Typical applications include autonomous robots, smart sensors, medical devices, and smart manufacturing.
Embedded AI is seen as the next stage in the evolution of embedded control networks. Back in the early days of AI, AI relied on large clusters of remote computers with high energy consumption and was fed by sensors in embedded networks, which were computed in the cloud and sent back to front-end actuators for control.
However, with the increasing demand for real-time performance, cloud-based architectures are no longer sufficient for latency-sensitive embedded control scenarios that must respond quickly. This has led the industry to deploy Edge AI ControllerThe AI calculations are performed closer to the scene to minimize latency.
The next step for embedded control networks is to allow AI to be decentralized to the AI-based sensors and AI-based actuators themselves, allowing them to choose whether or not to communicate with local edge AI controllers on demand, making the entire system more flexible and autonomous.
In the development of new technologies, centralized architectures are often the starting point; as they mature, they usually move towards decentralized architectures. The benefits of decentralization are clear: trained, pre-processed data is smaller, and true real-time communication is possible even on low-bandwidth networks.
This is crucial for all autonomous equipment, such as AGVs, AMRs, drones, self-driving agricultural machinery, forestry equipment, construction machinery, earthmoving equipment, etc.
Looking back at historical applications, we can see that these autonomous systems have generally adopted a distributed network architecture based on CAN buses; this is also true in areas such as medical equipment and laboratory equipment. In addition, the decentralized architecture brings an extra security advantage: Embedded AI networks do not have to rely on remote interfaces, so they are more immune to external network attacks, which significantly improves system security.
