TinyML Model for Classifying Hazardous Volatile Organic Compounds Using Low-Power Embedded Edge Sensors: Perfecting Factory 5.0 Using Edge AI

Abstract

In this letter, we present an intelligent chemical sniffer, capable of detecting hazardous volatile organic compounds (VOCs) using artificial intelligence deployed at the edge. The proposed solution utilizes a multichannel metal-oxide-semiconductor gas sensor for generating unique signature responses from the organic compounds. These signature responses are then used for training a quantized neural network model in the cloud and then deployed onto an embedded development platform for edge inference. In this approach, we utilized TensorFlow Lite for microcontrollers, an edge computing framework (developed by Google) to quantize a 32-bit floating point precision neural network model into an 8-bit integer precision model for deployment on low-power and low-memory footprint embedded edge devices. Our quantized model was able to successfully classify all the six VOCs (i.e., Xylene, Hexane, Acetone, Toluene, Methanol, and Butanol) with accuracies of 99.8 and 100% on the validation and test datasets, respectively. More than 50% reduction in on-device RAM and Flash memory usage were measured for the 8-bit quantized model when compared with the equivalently performing 32-bit floating point model for relatively same inference speed on the edge sensing device. The proposed solution paves the way for the mass-scale deployment of such intelligent chemical sensing solutions in the next-generation automated smart factories.