  Edge audio devices can reduce data bandwidth requirements by pre-processing
input speech on the device before transmission to the cloud. As edge devices
are required to ensure always-on operation, their stringent power constraints
pose several design challenges and force IC designers to look for solutions
that use low standby power. One promising bio-inspired approach is to combine
the continuous-time analog filter channels with a small memory footprint deep
neural network that is trained on edge tasks such as keyword spotting, thereby
allowing all blocks to be embedded in an IC. This paper reviews the historical
background of the continuous-time analog filter circuits that have been used as
feature extractors for current edge audio devices. Starting from the
interpretation of a basic biquad filter as a two-integrator-loop topology, we
introduce the progression in the design of second-order low-pass and band-pass
filters ranging from OTA-based to source-follower-based architectures. We also
derive and analyze the small-signal transfer function and discuss their usage
in edge audio applications.
