Edge AI refers to the deployment of artificial intelligence algorithms on local devices, rather than on centralized servers. This setup requires specific types of hardware that can perform data processing and inference close to where the data is generated or used. The primary hardware components used for Edge AI include edge devices, specialized processors, and sensors.
Edge devices encompass a variety of hardware types such as gateways, IoT devices, and smartphones. For example, a smart camera can process video feeds locally to detect motion or recognize faces without needing to send all the data to a cloud server. Another common example is an industrial IoT sensor that analyzes data in real time to monitor equipment health and predict maintenance needs. These devices must be equipped with sufficient computing power and memory to handle AI tasks efficiently while also being power-efficient to extend battery life in mobile applications.
Specialized processors play a critical role in Edge AI by providing the necessary power for AI computations. These can include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and graphics processing units (GPUs). An example of an ASIC used for Edge AI is the Google Coral Edge TPU, designed for quick inferencing tasks. FPGAs, like the Xilinx Versal AI Core, offer flexibility for customized hardware acceleration. In summary, various edge devices along with specialized processors like FPGAs and ASICs make up the hardware landscape for Edge AI, enabling efficient and real-time decision-making at the source of data generation.