{"id":1060059,"date":"2024-07-24T09:46:05","date_gmt":"2024-07-24T16:46:05","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=1060059"},"modified":"2024-07-24T09:46:05","modified_gmt":"2024-07-24T16:46:05","slug":"knowledge-boosting-during-low-latency-inference","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/knowledge-boosting-during-low-latency-inference\/","title":{"rendered":"Knowledge boosting during low-latency inference"},"content":{"rendered":"

Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time requirements and does not guarantee that both models will operate on the same data at the same time. We propose knowledge boosting, a novel technique that allows a large model to operate on time-delayed input during inference, while still boosting small model performance. Using a streaming neural network that processes 8~ms chunks, we evaluate different speech separation and enhancement tasks with communication delays of up to six chunks or 48~ms. Our results show larger gains where the performance gap between the small and large models is wide, demonstrating a promising method for large-small model collaboration for low-latency applications. Code, dataset, and audio samples available at\u00a0https:\/\/knowledgeboosting.cs.washington.edu\/ (opens in new tab)<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Models for low-latency, streaming applications could benefit from the knowledge capacity of larger models, but edge devices cannot run these models due to resource constraints. A possible solution is to transfer hints during inference from a large model running remotely to a small model running on-device. However, this incurs a communication delay that breaks real-time 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