Hardware Integration of a Neural Network onto FPGA and its Application to Sound Source Localization
Year: 2024
Type: conference-paper
Abstract: Sound Source Localization algorithms estimate the Direction of Arrival of one or multiple (moving) sound sources in a 3-D space. Potential applications include environment acoustic mapping, spatial filtering of relevant sources out of acoustic clutter and noise, and headset-free human-robot speech interaction. Deep Learning algorithms have been widely used, with many solutions relying on Feed-forward Neural Networks, convolutional kernels, or attention mechanisms derived from the Transformer. In this paper, the hardware implementation of a Deep Learning model for Sound Source Localization is proposed on FPGA evaluation board (Digilent Zybo-7020). This enables Artificial Intelligence inference directly onto a FPGA with acceptable performances (70 % accuracy for an error$< 10^{\circ}$) through an energy-efficient full hardware implementation, without resorting to usual processing units (e.g., CPU, GPU, DSP, etc.) nor soft-core processor with hardwired accelerators/co-processors.
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405735909