Ultra-Tiny Quantized Neural Networks for Piezo Pressure Sensors

Quantization is a technique that maps the representation of 32-bit floating point numerical values to a reduced set of discrete values. It is a fundamental asset for tiny machine learning developments aiming to bring artificial intelligence capabilities to the edge. It eases embedded devices with limited assets, such as microcontrollers to infer intelligent workloads close to sensors, with minimal memory footprint and computational capabilities. MEMS piezo pressure sensors are widespread today in a wide variety of applications. Unfortunately, they are affected by a broad range of stress conditions that ultimately cause sensor measurements to drift. This work investigated how to compensate such a drift for a MEMS pressure sensor using ipo-parameterized (101 parameters) and quantized convolutional neural networks which can be easily integrated on a microcontroller or even into the same sensor package. The models were trained to predict the pressure errors introduced by three case studies applied to a state-of-the-art sensor subject of stress conditions and to compensate for the sensor measurements. The models were designed with the same topology in terms of the quantization layers and by using a mix of activation and weight quantizers such as binary and 24 -bit fixed-point formats. The neural networks were trained by adopting the quantization-aware scheme of the QKeras framework. Prominent memory savings were achieved by these models compared to the full precision 32-bit floating point ones while offering gains in the accuracy of the predictions.