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.
TinyML Acceleration with MAX78000
Dabbous A.;Lazzaroni L.;Bellotti F.;Pighetti A.;Berta R.
2025-01-01
Abstract
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.
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