Summary of Resource-efficient Sensor Fusion Via System-wide Dynamic Gated Neural Networks, by Chetna Singhal and Yashuo Wu and Francesco Malandrino and Sharon Ladron De Guevara Contreras and Marco Levorato and Carla Fabiana Chiasserini
Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networks
by Chetna Singhal, Yashuo Wu, Francesco Malandrino, Sharon Ladron de Guevara Contreras, Marco Levorato, Carla Fabiana Chiasserini
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Networking and Internet Architecture (cs.NI)
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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 This paper addresses the challenge of optimizing AI-based applications in mobile systems that rely on heterogeneous data sources and distributed processing within the network. To minimize the cost of AI inference, the authors propose a novel algorithmic strategy called Quantile-constrained Inference (QIC), which jointly optimizes sensor selection, DNN architecture, node execution, and resource allocation. QIC leverages dynamic gated neural networks with branches and quantile-Constrained policy optimization to achieve high-quality, swift decisions that minimize inference energy cost. The authors evaluate QIC using a dynamic gated DNN trained on the RADIATE dataset and real-world wireless measurements, demonstrating significant performance improvements over alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making artificial intelligence work better in mobile devices like smartphones. Right now, these devices are getting more powerful, but they still need to process lots of data from different sources like cameras, radar, and lidar. To make this happen efficiently, the authors developed a new way to optimize how AI is used in these systems. They call it Quantile-constrained Inference (QIC), which is really good at making decisions quickly and using as little energy as possible. The authors tested QIC with some real-world data and showed that it works much better than other methods. |
Keywords
» Artificial intelligence » Inference » Optimization
