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Typical bio-signal processing front-ends are designed to maximize the qualityof the recorded data, to allow faithful reproduction of the signal formonitoring and off-line processing. This leads to designs that have relativelylarge area and power consumption figures. However, wearable devices foralways-on biomedical applications do not necessarily require to reproducehighly accurate recordings of bio-signals, provided their end-to-endclassification or anomaly detection performance is not compromised. Within thiscontext, we propose an adaptive Asynchronous Delta Modulator (ADM) circuitdesigned to encode signals with an event-based representation optimally suitedfor low-power on-line spiking neural network processors. The novel aspect ofthis work is the adaptive thresholding feature of the ADM, which allows thecircuit to modulate and minimize the rate of events produced with the amplitudeand noise characteristics of the signal. We describe the circuit's basic modeof operation, we validate it with experimental results, and characterize thenew circuits that endow it with its adaptive thresholding properties.

An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge