# Global-Local Convolution with Spiking Neural Networks for Energy-efficient Keyword Spotting

arXiv: https://arxiv.org/abs/2406.13179 · HTML: https://mlsanity.com/paper/2406.13179/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks](https://mlsanity.com/paper/2103.12593/) (2021)
- [Efficient Neuromorphic Signal Processing with Loihi 2](https://mlsanity.com/paper/2111.03746/) (2021)
- [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding](https://mlsanity.com/paper/2207.02971/) (2022)
- [TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks](https://mlsanity.com/paper/2206.10177/) (2022)
- [Low-activity supervised convolutional spiking neural networks applied to speech commands recognition](https://mlsanity.com/paper/2011.06846/) (2020)
- [Event-Driven Learning for Spiking Neural Networks](https://mlsanity.com/paper/2403.00270/) (2024)
- [An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks](https://mlsanity.com/paper/1909.01302/) (2019)
- [WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting](https://mlsanity.com/paper/2111.01456/) (2021)
- [CaTT-KWS: A Multi-stage Customized Keyword Spotting Framework based on Cascaded Transducer-Transformer](https://mlsanity.com/paper/2207.01267/) (2022)
- [Long Short-term Memory with Two-Compartment Spiking Neuron](https://mlsanity.com/paper/2307.07231/) (2023)

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

- [Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling](https://mlsanity.com/paper/2509.17186/) (2025)
- [SpikCommander: A High-performance Spiking Transformer with Multi-view Learning for Efficient Speech Command Recognition](https://mlsanity.com/paper/2511.07883/) (2025)
- [Adaptive Speech-to-Spike Encoding for Spiking Neural Networks](https://mlsanity.com/paper/2606.19039/) (2026)
- [Latency and accuracy tradeoffs in Spiking Neural Networks](https://mlsanity.com/paper/2609.35260/) (2026)

## Abstract (arXiv)

Thanks to Deep Neural Networks (DNNs), the accuracy of Keyword Spotting (KWS) has made substantial progress. However, as KWS systems are usually implemented on edge devices, energy efficiency becomes a critical requirement besides performance. Here, we take advantage of spiking neural networks' energy efficiency and propose an end-to-end lightweight KWS model. The model consists of two innovative modules: 1) Global-Local Spiking Convolution (GLSC) module and 2) Bottleneck-PLIF module. Compared to the hand-crafted feature extraction methods, the GLSC module achieves speech feature extraction that is sparser, more energy-efficient, and yields better performance. The Bottleneck-PLIF module further processes the signals from GLSC with the aim to achieve higher accuracy with fewer parameters. Extensive experiments are conducted on the Google Speech Commands Dataset (V1 and V2). The results show our method achieves competitive performance among SNN-based KWS models with fewer parameters.