Summary of Knowledge Boosting During Low-latency Inference, by Vidya Srinivas et al.
Knowledge boosting during low-latency inference
by Vidya Srinivas, Malek Itani, Tuochao Chen, Sefik Emre Eskimez, Takuya Yoshioka, Shyamnath Gollakota
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 research proposes a novel technique called knowledge boosting to facilitate collaboration between large models running remotely and small models running on edge devices. The goal is to improve the performance of small models while maintaining real-time requirements for low-latency applications. The approach involves allowing large models to operate on time-delayed input during inference, which enables more effective knowledge transfer to small models. Experimental results demonstrate promising gains in speech separation and enhancement tasks with communication delays up to 48 ms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where devices can process information quickly and accurately, without needing powerful computers or internet connections. That’s the goal of this research! Scientists have developed a way for small devices (like smartphones) to work together with big computers in the cloud to get better results. They call it “knowledge boosting”. It lets the big computer send hints to the small device, which helps the device make smarter decisions. In this case, they tested it on speech recognition and enhancement tasks, like separating different voices or cleaning up noisy audio. The results show that this technique can really improve performance! |
Keywords
* Artificial intelligence * Boosting * Inference
