Benchmarking Microcontrollers with Ultra-Low Resolution Images Classification

The advancement of edge devices equipped with specialized hardware accelerators, data caches, and microcontroller units (MCUs) has brought the deployment and execution of Deep Neural Network (DNN) models closer to users and real-world sensor systems. This paper explores the potential of various specialized MCUs across four real-world applications (waste classification, presence detection, miniature robot detection, and sign language interpretation). We evaluate three well-known MCU (STM32H7, Arduino, and MAX78000) comparing their inference time and power/energy consumption on four ultra-low resolution image-classification datasets with varying input and task complexities. Our findings indicate that all MCUs deliver excellent performance in terms of inference time and energy consumption for real-time applications, with the MAX78000 outperforming the others in all the metrics considered, thanks to the use of a DNN accelerator.