Design and Implementation of Tiny ML Model Using STM32F Platform

Tiny Machine Learning (Tiny ML) is gaining popularity for deploying lightweight ML models on resource-constrained devices like microcontrollers. The main challenge lies in creating efficient models due to limited resources and a lack of standardized tools. The STM 32F platform, known for low power consumption and real-time processing, is ideal for low-latency applications. By developing Tiny ML Models on this platform, developers can enable real-time applications in areas like automotive, healthcare, and industrial automation. An innovative approach involves using TensorFlow Lite for model compression, fitting ML algorithms into microcontroller memory, and optimizing performance for edge computing applications, such as camera-based object detection. This STM 32F-based model aims to demonstrate an end-to-end Tiny ML product development flow by enhancing the efficiency of standardized templates in terms of computation and memory requirements. In order to choose memory-efficient model designs and meet computing and memory requirements, data preprocessing, quantization, loop unrolling, and memory management must be optimized while weighing the trade-off between accuracy and resource consumption.