Efficient implementation of an audio preprocessing algorithm for SNN keyword spotting

Abstract

Mobile devices are getting increasingly powerful, becoming compatible
for an ever increasing set of functionality. Applications based around
neural networks however still have to offload parts of their computations
to the cloud since current Artificial Neural Networks (ANNs) are
still too computationally expensive for any practical standalone use in
energy constrained mobile devices. Developments in the next generation
of ANN: Spiking Neural Network (SNN), are expected to bring
neural networks directly to the edge. Even though SNNs are becoming
a reality, they can not (yet) effectively operate on raw sensory input
data. For this, a preprocessing algorithm can be used to extract low-level
features in an efficient way to boost the neural network efficiency.
A parallel can be found in biology with the cochlea that, for audio,
provides preprocessing for the brain. Recent research has shown that
an SNN is capable of reaching high classification accuracy when combined
with an biologically plausible audio preprocessing stage. To be
of interest for edge-computing it however also needs to be area and
energy efficient. This thesis will provide the first steps in researching
the optimal configuration of a specific audio preprocessing algorithm
by mapping its current software simulation to embedded hardware.
For this purpose the software simulation is analyzed and an efficient
hardware implementation is designed. For evaluation a prototype,
and its hardware constrained simulation, is developed and optimized.