  Typical bio-signal processing front-ends are designed to maximize the quality
of the recorded data, to allow faithful reproduction of the signal for
monitoring and off-line processing. This leads to designs that have relatively
large area and power consumption figures. However, wearable devices for
always-on biomedical applications do not necessarily require to reproduce
highly accurate recordings of bio-signals, provided their end-to-end
classification or anomaly detection performance is not compromised. Within this
context, we propose an adaptive Asynchronous Delta Modulator (ADM) circuit
designed to encode signals with an event-based representation optimally suited
for low-power on-line spiking neural network processors. The novel aspect of
this work is the adaptive thresholding feature of the ADM, which allows the
circuit to modulate and minimize the rate of events produced with the amplitude
and noise characteristics of the signal. We describe the circuit's basic mode
of operation, we validate it with experimental results, and characterize the
new circuits that endow it with its adaptive thresholding properties.
