This paper proposes a novel low-power HW accelerator for audio PDM-to-PCM conversion based on artificial neural network. The system processes samples from a digital MEMS microphone and converts them in PCM format by using a 1-Dimensional Convolutional Neural Network (1D-CNN). The model has been quantized to reduce the computational complexity while preserving its Signal-to-Noise Ratio (SNR) and the HW accelerator has been designed to minimize the physical resources. The SNR achieved is 41.56 dB while the prototyping of the design on a Xilinx Artix-7 FPGA shows a dynamic power consumption of 1 mW and a utilization of 606 LUTs and 410 FFs. These results enable the proposed system to be the first step of a tiny low-power end-to-end neural network-based Keyword Spotting (KWS) system.

Quantized ID-CNN for a Low-power PDM-to-PCM Conversion in TinyML KWS Applications

Vitolo P.;Licciardo G. D.
;
Di Benedetto L.;Liguori R.;Rubino A.;
2022

Abstract

This paper proposes a novel low-power HW accelerator for audio PDM-to-PCM conversion based on artificial neural network. The system processes samples from a digital MEMS microphone and converts them in PCM format by using a 1-Dimensional Convolutional Neural Network (1D-CNN). The model has been quantized to reduce the computational complexity while preserving its Signal-to-Noise Ratio (SNR) and the HW accelerator has been designed to minimize the physical resources. The SNR achieved is 41.56 dB while the prototyping of the design on a Xilinx Artix-7 FPGA shows a dynamic power consumption of 1 mW and a utilization of 606 LUTs and 410 FFs. These results enable the proposed system to be the first step of a tiny low-power end-to-end neural network-based Keyword Spotting (KWS) system.
978-1-6654-0996-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4805931
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