What is an e-AI solution?
Anyone can use AI (Artificial Intelligence) relatively easily by using Caffe developed by UC Berkeley or TensorFlow developed by Google. Although AI's specialty field varies according to the algorithm used, DNN (Deep Neural Network), a multilayered network, is used for AI that became famous for the game “Go” and image identification. Through an algorithm that learns input information that is labeled (called teacher data) so that the estimation results appearing in output coincide with each other, and thanks to the technical breakthrough of multilayering and automated feature extraction, DNN has dramatically improved estimation accuracy. DNN has a large difference in the amount of computation required for learning and inference execution, and it is a major feature that it can be executed with less computing power in the inference phase.
It is necessary to calculate to decide unknown coefficients by inputting an enormous amount of learning object data in the learning phase, and there is a tendency to implement it using huge computing power such as a server.
Focusing on the asymmetry of this computing power and for its main use for inference execution in embedded devices, we named this AI "e-AI" (embedded-AI).
Renesas provides the e-AI development environment to realize AI implementation easier for MCU or AI accelerator.
When user inputs the trained AI model into this tool, then the tool converts to a program that runs on the MCU or AI accelerator without any additional operations.