# Perforated Neural Networks for Keyword Spotting

arXiv: https://arxiv.org/abs/2605.15647 · HTML: https://mlsanity.com/paper/2605.15647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 15 May 2026.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Multi-Time Attention Networks for Irregularly Sampled Time Series](https://mlsanity.com/paper/2101.10318/) (2021)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information](https://mlsanity.com/paper/2110.13716/) (2021)
- [Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks](https://mlsanity.com/paper/2106.07490/) (2021)
- [Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural Networks](https://mlsanity.com/paper/2501.18018/) (2025)
- [Exploring the Performance of Perforated Backpropagation through Further Experiments](https://mlsanity.com/paper/2506.00356/) (2025)

## Abstract (arXiv)

Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and non-negotiable accuracy thresholds must all be satisfied simultaneously. Existing compression and optimization techniques can trade one resource for another, but rarely improve both accuracy and model size at the same time. This paper presents the application of Perforated Backpropagation to keyword spotting on the Edge Impulse platform, an experiment that won the Best Model award at the Edge Impulse 2025 Hackathon in December 2025. By adding artificial Dendrite Nodes to a standard convolutional neural network trained on the Edge Impulse keyword spotting tutorial pipeline, we demonstrate that dendritic models outperform traditional architectures at every level of parameter count and at every accuracy threshold tested across 800 hyperparameter trials. The best dendritic model achieved a test accuracy of 0.933 with only 1,500 parameters, versus the baseline accuracy of 0.921 requiring approximately 4,000 parameters. These results suggest that Perforated Backpropagation is a powerful addition to the edge AI engineer's toolkit, offering simultaneous gains in both model quality and deployment efficiency.