Machine Learning

Microchip Technology Machine Learning (ML) involves selecting software and hardware tool kits, reference designs, and silicon platforms where a simplified, easy-to-use environment with advanced performance is required. Microchip Technology ML algorithms are easy to collect and organize data, train neural networks in data centers, or implement optimized inference on the edge. The extensive Microchip portfolio of silicon devices includes microcontrollers (MCUs), microprocessors (MPUs), and Field-Programmable Gate Arrays (FPGAs). The software toolkits allow the use of popular ML frameworks, including TensorFlow, Keras, Caffe, and many others covered by the ONNX umbrella, as well as those found within TinyML and TensorFlow Lite.