A New NN-Based Approach to In-Sensor PDM-to-PCM Conversion for Ultra TinyML KWS

This brief proposes a new approach based on a tiny neural network to convert Pulse Density Modulation (PDM) signals acquired from digital Micro-Electro-Mechanical System (MEMS) microphones into the standard Pulse Code Modulation (PCM) format for any further digital audio processing. The proposed approach allows for a compact and ultra-low-power hardware implementation of the conversion, suitable for ultra tinyML Key Word Spotting (KWS) applications, closely coupled with the sensor itself and tightly coupled with a neural network classifier. The converter achieves a signal-to-noise ratio value of 48 dB, which enables KWS accuracy of 89% over 12 classes. Implementation on a Xilinx Artix-7 FPGA results in 917 LUTs, 361 FFs, and 182 <tex-math notation="LaTeX">μ W </tex-math> Dynamic Power (DynP) consumption. By targeting the TSMC 0.13 <tex-math notation="LaTeX">μ m </tex-math> CMOS technology, the synthesis reports an area occupation of 0.086 mm2 and a DynP of 128.7 <tex-math notation="LaTeX">μ W </tex-math> /MHz. These results enable the integration of the proposed design into the CMOS circuitry closely coupled with the MEMS microphone.