# Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computing

arXiv: https://arxiv.org/abs/2501.10245 · HTML: https://mlsanity.com/paper/2501.10245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Wireless Image Retrieval at the Edge](https://mlsanity.com/paper/2007.10915/) (2020)
- [Task-Oriented Communication for Multi-Device Cooperative Edge Inference](https://mlsanity.com/paper/2109.00172/) (2021)
- [Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy](https://mlsanity.com/paper/1806.07840/) (2018)
- [Blind Federated Edge Learning](https://mlsanity.com/paper/2010.10030/) (2020)
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [Neural Network Compression for Noisy Storage Devices](https://mlsanity.com/paper/2102.07725/) (2021)
- [Memristor models for machine learning](https://mlsanity.com/paper/1406.2210/) (2014)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics](https://mlsanity.com/paper/2209.10481/) (2022)

## Abstract (arXiv)

Deep neural networks provide reliable solutions for many classification and regression tasks; however, their application in real-time wireless systems with simple sensor networks is limited due to high energy consumption and significant bandwidth needs. This study proposes a multi-sensor wireless inference system with memristor-based analog computing. Given the sensors' limited computational capabilities, the features from the network's front end are transmitted to a central device where an Lp-norm inspired approximation of the maximum operation is employed to achieve transformation-invariant features, enabling efficient over-the-air transmission. We also introduce a trainable over-the-air sensor fusion method based on Lp-norm inspired combining function that customizes sensor fusion to match the network and sensor distribution characteristics, enhancing adaptability. To address the energy constraints of sensors, we utilize memristors, known for their energy-efficient in-memory computing, enabling analog-domain computations that reduce energy use and computational overhead in edge computing. This dual approach of memristors and Lp-norm inspired sensor fusion fosters energy-efficient computational and transmission paradigms and serves as a practical energy-efficient solution with minimal performance loss.