Hardware Platforms for Tiny Machine Learning

Abstract

Tiny Machine Learning (TinyML) integrates machine learning functionality with low-power, resource-limited devices, such as microcontrollers and edge devices, to enable smart Internet of Things and edge use cases. As the Internet of Things and edge computing surge, TinyML unlocks opportunities in smart wearables, industrial predictive maintenance, precision agriculture, and autonomous devices. This chapter presents a comprehensive analysis of the hardware platforms powering TinyML, emphasizing their computation speed, power consumption, memory requirements, and ease of integration with machine learning models. The ARM Cortex-series, ESP32, Raspberry Pi, and AI accelerators like Google Edge TPU and NVIDIA Jetson Nano are regarded highly as the market leaders in this domain. Each platform has specific advantages according to its respective application requirements, like latency, power consumption, and inference rate. The trade-offs are highlighted in the analysis presented. By integrating real-world case studies and benchmarking metrics, this chapter equips developers with actionable insights to navigate design trade-offs, optimize resource allocation, and select platforms aligned with deployment goals. Ultimately, this resource empowers engineers and researchers to harness TinyML's potential, fostering innovation in scalable, sustainable, intelligent edge solutions.