This paper presents a real-time, energy-efficient embedded system implementing an array of Cascade of Asymmetric Resonators with Fast-Acting Compression (CARFAC) cochlea models for underwater sound analysis. Built on the AMD Kria KV260 System-on-Module (SoM), the system integrates a Rust-based software framework on the processor for real-time interfacing and synchronization with multiple hydrophone inputs, and a hardware-accelerated implementation of the CARFAC models on a Field-Programmable Gate Array (FPGA) for real-time sound pre-processing. Compared to prior work, the CARFAC accelerator achieves improved scalability and processing speed while reducing resource usage through optimized time-multiplexing, pipelined design, and elimination of costly division circuits. Experimental results demonstrate 13.5% hardware utilization for a single 64-channel CARFAC instance and a whole board power consumption of 3.11 W when processing a 256 kHz input signal in real time.
@article{arxiv.2508.07523,
title = {Real-time CARFAC Cochlea Model Acceleration on FPGA for Underwater Acoustic Sensing Systems},
author = {Bram Bremer and Matthew Bigelow and Stuart Anstee and Gregory Cohen and Andre van Schaik and Ying Xu},
journal= {arXiv preprint arXiv:2508.07523},
year = {2025}
}