# Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck

arXiv: https://arxiv.org/abs/2404.01804 · HTML: https://mlsanity.com/paper/2404.01804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications](https://mlsanity.com/paper/1910.01059/) (2019)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck](https://mlsanity.com/paper/2106.01177/) (2021)
- [Neuromorphic Integrated Sensing and Communications](https://mlsanity.com/paper/2209.11891/) (2022)
- [Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip](https://mlsanity.com/paper/2004.01454/) (2020)
- [End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence](https://mlsanity.com/paper/2009.01527/) (2020)
- [Towards Efficient and Reliable AI Through Neuromorphic Principles](https://mlsanity.com/paper/2309.15942/) (2023)

## Built on by (most cited first, 1 of 1)

- [Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications](https://mlsanity.com/paper/2510.14688/) (2025)

## Abstract (arXiv)

An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server. The device carries out data collection and partial processing of the data, while the remote server completes the given task based on information received from the device. It is often required that processing and communication be run as efficiently as possible at the device, while more computing resources are available at the edge. To address such scenarios, we introduce a new system solution, termed neuromorphic wireless device-edge co-inference. According to it, the device runs sensing, processing, and communication units using neuromorphic hardware, while the server employs conventional radio and computing technologies. The proposed system is designed using a transmitter-centric information-theoretic criterion that targets a reduction of the communication overhead, while retaining the most relevant information for the end-to-end semantic task of interest. Numerical results on standard data sets validate the proposed architecture, and a preliminary testbed realization is reported.