A Collaborative Multi-Compression Acceleration Mechanism for Neural Networks in Keyword Spotting

Abstract

To address the large model size, high computational cost, and limited deployment resources of keyword spotting models on edge platforms, this study proposes a collaborative multi-compression framework for lightweight deployment. Built on LiteKWS-Net, an attention-enhanced 2-D convolutional backbone, the framework combines adaptive importance-aware structured pruning, mixed-precision quantization, and quantization-aware multi-stage knowledge distillation. The retrained teacher reaches 97.90% (mean, 100,813 parameters, 0.385 MiB). MPDQ reaches 95.53 ± 1.16% at 8.27× theoretical weight compression. AIASP reaches 97.59% at a 30% target and 43.9% realized sparsity. The final joint model reaches 96.82% and, under ideal packed sparse mixed-precision storage, has a 51.55× theoretical weight-compression factor relative to the FP32 teacher; sparse-index overhead is excluded. On a Jetson Nano, the TensorRT FP16 network-body benchmark reports 2.86 ms latency and 0.69 mJ per inference.