# LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting

arXiv: https://arxiv.org/abs/2211.04635 · HTML: https://mlsanity.com/paper/2211.04635/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://mlsanity.com/paper/2104.00769/) (2021)
- [Temporal Convolution for Real-time Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1904.03814/) (2019)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [Streaming keyword spotting on mobile devices](https://mlsanity.com/paper/2005.06720/) (2020)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1703.05390/) (2017)
- [End-to-End Streaming Keyword Spotting](https://mlsanity.com/paper/1812.02802/) (2018)
- [Efficient keyword spotting using dilated convolutions and gating](https://mlsanity.com/paper/1811.07684/) (2018)
- [Attention-based End-to-End Models for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1803.10916/) (2018)
- [Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1705.02411/) (2017)
- [Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution](https://mlsanity.com/paper/2010.09960/) (2020)
- [Stacked 1D convolutional networks for end-to-end small footprint voice trigger detection](https://mlsanity.com/paper/2008.03405/) (2020)

## Built on by (most cited first, 4 of 4)

- [End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers](https://mlsanity.com/paper/2509.07051/) (2025)
- [Efficient Continual Learning in Keyword Spotting using Binary Neural Networks](https://mlsanity.com/paper/2505.02469/) (2025)
- [Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches](https://mlsanity.com/paper/2302.08950/) (2023)
- [Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning](https://mlsanity.com/paper/2409.00099/) (2024)

## Abstract (arXiv)

This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units like microcontrollers. ML operators exhibit heterogeneous efficiency profiles on power-efficient hardware. Given the exact theoretical computation cost, int8 operators are more computation-effective than float operators, and linear layers are often more efficient than other layers. The proposed LiCo-Net is a dual-phase system that uses the efficient int8 linear operators at the inference phase and applies streaming convolutions at the training phase to maintain a high model capacity. The experimental results show that LiCo-Net outperforms single-value decomposition filter (SVDF) on hardware efficiency with on-par detection performance. Compared to SVDF, LiCo-Net reduces cycles by 40% on HiFi4 DSP.