# Towards Battery-Free Machine Learning and Inference in Underwater Environments

arXiv: https://arxiv.org/abs/2202.08174 · HTML: https://mlsanity.com/paper/2202.08174/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 16 Feb 2022.

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

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Up or Down? Adaptive Rounding for Post-Training Quantization](https://mlsanity.com/paper/2004.10568/) (2020)
- [Post-Training Quantization for Vision Transformer](https://mlsanity.com/paper/2106.14156/) (2021)
- [Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning](https://mlsanity.com/paper/2006.12097/) (2020)
- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments](https://mlsanity.com/paper/2004.05703/) (2020)
- [A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics](https://mlsanity.com/paper/1703.02952/) (2017)
- [Intelligence Beyond the Edge: Inference on Intermittent Embedded Systems](https://mlsanity.com/paper/1810.07751/) (2018)
- [Privacy-preserving Voice Analysis via Disentangled Representations](https://mlsanity.com/paper/2007.15064/) (2020)
- [Semi-supervised Federated Learning for Activity Recognition](https://mlsanity.com/paper/2011.00851/) (2020)
- [LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement](https://mlsanity.com/paper/2105.01299/) (2021)
- [Intermittent Learning: On-Device Machine Learning on Intermittently Powered System](https://mlsanity.com/paper/1904.09644/) (2019)
- [LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations](https://mlsanity.com/paper/2001.10189/) (2020)
- [Seabed classification using physics-based modeling and machine learning](https://mlsanity.com/paper/2003.11156/) (2020)

## Built on by (most cited first, 9 of 9)

- [Benchmarking Ultra-Low-Power μNPUs](https://mlsanity.com/paper/2503.22567/) (2025)
- [The Case for Hierarchical Deep Learning Inference at the Network Edge](https://mlsanity.com/paper/2304.11763/) (2023)
- [Low-Energy On-Device Personalization for MCUs](https://mlsanity.com/paper/2403.08040/) (2024)
- [Energy-Aware Deep Learning on Resource-Constrained Hardware](https://mlsanity.com/paper/2505.12523/) (2025)
- [HyperCam: Low-Power Onboard Computer Vision for IoT Cameras](https://mlsanity.com/paper/2501.10547/) (2025)
- [InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs](https://mlsanity.com/paper/2603.04134/) (2026)
- [Towards Machine Learning and Inference for Resource-constrained MCUs](https://mlsanity.com/paper/2305.18954/) (2023)
- [Energy-Aware Dynamic Neural Inference](https://mlsanity.com/paper/2411.02471/) (2024)
- [Towards Low-Energy Adaptive Personalization for Resource-Constrained Devices](https://mlsanity.com/paper/2403.15905/) (2024)

## Abstract (arXiv)

This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific exploration, and climate/weather prediction. To answer this question, we explore the feasibility of bridging advances from the past decade in two fields: battery-free networking and low-power machine learning. Our exploration demonstrates that it is indeed possible to enable battery-free inference in underwater environments. We designed a device that can harvest energy from underwater sound, power up an ultra-low-power microcontroller and on-board sensor, perform local inference on sensed measurements using a lightweight Deep Neural Network, and communicate the inference result via backscatter to a receiver. We tested our prototype in an emulated marine bioacoustics application, demonstrating the potential to recognize underwater animal sounds without batteries. Through this exploration, we highlight the challenges and opportunities for making underwater battery-free inference and machine learning ubiquitous.