Summary of Multitasc++: a Continuously Adaptive Scheduler For Edge-based Multi-device Cascade Inference, by Sokratis Nikolaidis et al.
MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference
by Sokratis Nikolaidis, Stylianos I. Venieris, Iakovos S. Venieris
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 presents MultiTASC++, a scheduler that optimizes the performance of multi-device cascade systems, which are used for distributed inference in intelligent indoor environments like smart homes. The system involves multiple devices using a shared heavy model hosted on a server to achieve high accuracy and low latency. The proposed scheduler dynamically controls the forwarding decision functions of devices to optimize system throughput while maintaining high accuracy. Experimental results demonstrate the efficacy of MultiTASC++ in diverse device environments, achieving a targeted satisfaction rate and providing the highest available accuracy across different device tiers and workloads. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about improving how smart home devices work together to make decisions using artificial intelligence. It introduces a new way for these devices to share information and get the best results while keeping things fast and accurate. The idea is to create a better system that can handle many devices working together, which is important for making smart homes more intelligent. |
Keywords
» Artificial intelligence » Inference
