Environment-Adaptable Edge-Computing Gas-Sensor Device With Analog-Assisted Continual Learning Scheme

This paper presents a multi-gas sensor device whose structure is optimized for edge computing capability under internet of things (IoT) environments. Considering inherent sensor device characteristics susceptible to environmental factors like temperature and humidity, edge-computing capability for the on-site sensor calibration and pattern recognition (PR) is facilitated through a proposed analog-assisted continual learning scheme. An environment-adaptable continual learning (EACL) is proposed to combine multiple learning processes under different environments including chamber and on-site. Its computation burden is much relieved to be integrated into the edge device by adopting the analog-assisted structure, where a designed readout integrated circuit (ROIC) for automatic calibration normalizes gas-sensor data. For functional feasibility, an edge-computing IoT device prototype is manufactured with a fabricated ROIC and an in-house semiconductor-type sensor array, supporting wireless on-site monitoring platform interfaces. The environment-adaptable edge-computing capability is functionally verified through EACL-PR experiments on hazardous gases such as NO 2 and CO under environmental factor variations. The average PR accuracy of 97% is achieved on several kinds of mixture gas patterns. The analog-assisted operation is verified to reduce the training cycles by 3 times while the EACL itself achieves 25% better efficiency.