# A Channel-Pruned and Weight-Binarized Convolutional Neural Network for Keyword Spotting

arXiv: https://arxiv.org/abs/1909.05623 · HTML: https://mlsanity.com/paper/1909.05623/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method](https://mlsanity.com/paper/2204.04375/) (2022)

## Abstract (arXiv)

We study channel number reduction in combination with weight binarization (1-bit weight precision) to trim a convolutional neural network for a keyword spotting (classification) task. We adopt a group-wise splitting method based on the group Lasso penalty to achieve over 50% channel sparsity while maintaining the network performance within 0.25% accuracy loss. We show an effective three-stage procedure to balance accuracy and sparsity in network training.