TinyML Acceleration with MAX78000

The advancement of edge devices equipped with specialized hardware accelerators has brought the deployment and execution of Deep Neural Network (DNN) models nearer to users and real-world sensor systems. This paper investigates the potential of the MAX78000 microcontroller in accelerating Tiny Machine Learning applications, which require real-time processing and low power consumption. We compare its performance against other platforms like the STM32H7 and Raspberry Pi 4, focusing on a case study involving the detection of miniature mobile robots using an ultra-low-resolution Time-of-Flight sensor. Despite slightly lower accuracy, the MAX78000 outperforms other platforms in terms of inference time, power, and energy consumption, making it a reliable choice for power-constrained applications.