On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level
  Sensor Data
Tianheng Ling Chao Qian Gregor Schiele
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.