SolarML

Abstract

Machine learning models can now run on microcontrollers. Thanks to the advances in neural architectural search, we can automatically identify tiny machine learning (tinyML) models that satisfy stringent memory and energy requirements. However, existing methods often overlook the energy used during event detection and data gathering. This is critical for devices powered by renewable energy sources like solar power, where energy efficiency is paramount. To address it, we introduce SolarML, a solution designed specifically for solar-powered tinyML platforms, which optimizes the end-to-end system's inference accuracy and energy consumption, from data gathering and processing to model inference. Considering two applications of gesture recognition and keywords spotting, SolarML has the following contributions: 1) a hardware platform with an optimal event detection mechanism that reduces event detection costs by up to 10 x compared to state-of-the-art alternatives; 2) a joint optimization framework eNAS that reduces the energy consumption of the sensor and inference model by up to 2 x, compared to methods that only optimize the inference model. Jointly, they enable SolarML to run end-to-end gesture and audio inference on a battery-free tinyML platform by only harvesting solar energy for 30 and 57 seconds, respectively, in an office environment (500 lux). Source code is available at [1].