On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
This addresses latency and efficiency challenges for autonomous systems using wireless sensor networks, though it is incremental in applying existing AI methods to on-device deployment.
The study tackled real-time fluid flow estimation from level sensor data by developing on-device soft sensors using AI on microcontrollers and FPGAs, achieving inference times as low as 1.04 microseconds.
Soft sensors are crucial in bridging autonomous systems' physical and digital realms, enhancing sensor fusion and perception. Instead of deploying soft sensors on the Cloud, this study shift towards employing on-device soft sensors, promising heightened efficiency and bolstering data security. Our approach substantially improves energy efficiency by deploying Artificial Intelligence (AI) directly on devices within a wireless sensor network. Furthermore, the synergistic integration of the Microcontroller Unit and Field-Programmable Gate Array (FPGA) leverages the rapid AI inference capabilities of the latter. Empirical evidence from our real-world use case demonstrates that FPGA-based soft sensors achieve inference times ranging remarkably from 1.04 to 12.04 microseconds. These compelling results highlight the considerable potential of our innovative approach for executing real-time inference tasks efficiently, thereby presenting a feasible alternative that effectively addresses the latency challenges intrinsic to Cloud-based deployments.