MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).
MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).
This is an open source project on the deployment of deep learning to embedded microprocessors. The project establishes a data set for obstacle recognition of blind travel environment, and trains a simplified MoblieNet model in TensorFlow. Finally, the binary file of the model is deployed on the UNCLEO-STM32H7A3ZIT-Q development board to realize the function of obstacle detection of blind travel environment.
This project focuses on the implementation of optimized Linear and DNN regression models for inter-vehicle distance prediction in a Cooperative Adaptive Cruise Control (CACC) application. It leverages Tensorflow Lite to create optimized models through quantization and pruning for realtime inferencing on Raspberry Pi and On-board Unit (OBU) of Connected Autonomous Vehicles.