Robust and Efficient Bio-Inspired Data-Sampling Prototype for Time-Series Analysis.

Data acquisition is crucial for efficient AI systems. We present a bio-inspired prototype implementation of discrepancy-based adaptive threshold-based sampling on a low-cost microcontroller. We show measurement results demonstrating that an adaptive threshold-based sampling approach can be performed only using onboard components of the microcontroller. To measure the sampling precision, we used sinusoidal signals output by a waveform generator and compared the signals after reconstruction to exact signals with the set parameters. These measurements show that, even with such low-cost components, discrepancy-based adaptive threshold-based sampling can be performed with high precision.