# Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications

arXiv: https://arxiv.org/abs/1903.01531 · HTML: https://mlsanity.com/paper/1903.01531/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

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

- [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://mlsanity.com/paper/2104.00769/) (2021)
- [Doping: A technique for efficient compression of LSTM models using sparse structured additive matrices](https://mlsanity.com/paper/2102.07071/) (2021)
- [Compilation and Optimizations for Efficient Machine Learning on Embedded Systems](https://mlsanity.com/paper/2206.03326/) (2022)
- [On the Expressive Power of Weight Quantization in Large Language Models](https://mlsanity.com/paper/2606.22249/) (2026)

## Abstract (arXiv)

Machine learning-based applications are increasingly prevalent in IoT devices. The power and storage constraints of these devices make it particularly challenging to run modern neural networks, limiting the number of new applications that can be deployed on an IoT system. A number of compression techniques have been proposed, each with its own trade-offs. We propose a hybrid network which combines the strengths of current neural- and tree-based learning techniques in conjunction with ternary quantization, and show a detailed analysis of the associated model design space. Using this hybrid model we obtained a 11.1% reduction in the number of computations, a 52.2% reduction in the model size, and a 30.6% reduction in the overall memory footprint over a state-of-the-art keyword-spotting neural network, with negligible loss in accuracy.