# ATM-Net: Adaptive Termination and Multi-Precision Neural Networks for Energy-Harvested Edge Intelligence

arXiv: https://arxiv.org/abs/2502.09822 · HTML: https://mlsanity.com/paper/2502.09822/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [NeuroShield: A Neuro-Symbolic Framework for Adversarial Robustness](https://mlsanity.com/paper/2601.13162/) (2026)
- [Energy Efficient Software Hardware CoDesign for Machine Learning: From TinyML to Large Language Models](https://mlsanity.com/paper/2603.23668/) (2026)
- [ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization](https://mlsanity.com/paper/2605.01866/) (2026)

## Abstract (arXiv)

ATM-Net is a novel neural network architecture tailored for energy-harvested IoT devices, integrating adaptive termination points with multi-precision computing. It dynamically adjusts computational precision (32/8/4-bit) and network depth based on energy availability via early exit points. An energy-aware task scheduler optimizes the energy-accuracy trade-off. Experiments on CIFAR-10, PlantVillage, and TissueMNIST show ATM-Net achieves up to 96.93% accuracy while reducing power consumption by 87.5% with Q4 quantization compared to 32-bit operations. The power-delay product improves from 13.6J to 0.141J for DenseNet-121 and from 10.3J to 0.106J for ResNet-18, demonstrating its suitability for energy-harvesting systems.