Summary of Neuromorphic Wireless Device-edge Co-inference Via the Directed Information Bottleneck, by Yuzhen Ke et al.
Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck
by Yuzhen Ke, Zoran Utkovski, Mehdi Heshmati, Osvaldo Simeone, Johannes Dommel, Slawomir Stanczak
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a novel system solution for device-edge co-inference, a crucial aspect of next-generation wireless systems. The proposed neuromorphic wireless device-edge co-inference (NWDEC) framework partitions semantic tasks between devices and edge servers. Devices collect data and partially process it, while remote servers complete the task using received information. To optimize processing and communication at the device, while leveraging more computing resources at the edge, NWDEC employs neuromorphic hardware for sensing, processing, and communication units, along with conventional radio and computing technologies at the server. The system is designed using a transmitter-centric information-theoretic criterion to minimize communication overhead while retaining relevant information for the end-to-end semantic task. Numerical results on standard datasets validate the architecture, and a preliminary testbed realization is presented. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to connect devices and computers in the future wireless networks. It’s like a special kind of teamwork where devices do some work and send it to a computer, which then finishes the job. The team wants to make this process more efficient by using special hardware that can learn and adapt. They created a system called NWDEC that combines this hardware with regular computers to make it all work together smoothly. They tested it on some standard datasets and found that it works well. This is an important step towards creating faster, more powerful wireless networks. |
Keywords
* Artificial intelligence * Inference
