Comparison of Hardware-friendly, Audio-to-spikes Cochlear Encoding for Neuromorphic Processing

Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks (SNNs) is a promising solution to lower energy consumption in Artificial Intelligence systems. The current rarity of natively neuromorphic datasets promotes the development of software tools to translate input sensory data into spikes. However, highly bio-mimetic simulators can be challenging to implement on digital hardware. In this work, we evaluate the impact of audio-to-spikes encoding on an audio classification task regarding its accuracy, energy efficiency, and ease of hardware implementation. We compare a state-of-the-art, bio-mimetic cochlear encoder to a lighter and more hardware-friendly model, on the benchmark datasets Heidelberg Digits and Google Speech Commands. Our work shows that the latter model can reach higher accuracy and energy efficiency, paving the way for better neuromorphic systems.