Knowledge boosting during low-latency inference
Proposes 'Knowledge Boosting' to enhance small models during low-latency inference using delayed hints from remote large models, achieving up to 3.53dB SI-SDR gain.
Key Findings
Methodology
This paper introduces 'Knowledge Boosting', a novel approach enabling a remote large model to provide delayed hints to a local small model during inference, despite communication latency. The core framework employs a streaming neural network based on TF-GridNet, which processes 8ms audio chunks in real-time. During training, the large model is pre-trained to produce stable intermediate embeddings, which are compressed and stored. In inference, these embeddings are transmitted with delay, then integrated into the small model via cross-attention modules that fuse delayed hints with current input features. The joint training process optimizes both models simultaneously, encouraging the large model to generate useful hints and the small model to effectively utilize them, even with delays up to 48ms. This architecture supports multi-task speech processing tasks such as speech enhancement, blind source separation, and target speech extraction, demonstrating significant performance improvements under realistic communication constraints.
Key Results
- Under a maximum delay of 48ms (C=6), the knowledge boosting approach improves SI-SDR by 0.23dB for speech enhancement, 2.31dB for source separation, and 3.53dB for target speech extraction, outperforming baseline models of similar size. For instance, in TSE, performance increased from 9.34dB to 7.70dB, indicating a substantial enhancement.
- Compared to baseline models with similar parameters, the proposed method reduces computational complexity (MACs) by approximately 50% while achieving better performance, demonstrating efficiency. The parameters are also halved, making it suitable for deployment on resource-constrained devices.
- Ablation studies reveal that larger delays cause performance drops, but joint training and delayed hints mitigate this effect. Compressing the transmitted embeddings (P=2 or 4) slightly reduces accuracy but maintains overall gains, validating the robustness of the approach.
Significance
This work addresses a critical challenge in deploying high-capacity models on edge devices with real-time constraints. By enabling remote large models to assist local small models through delayed hints, it bridges the gap between computational resource limitations and the need for high-quality speech processing. The method's ability to operate effectively under communication delays broadens the scope of real-world applications such as wireless earbuds, mobile assistants, and autonomous systems. It paves the way for scalable, efficient, and intelligent edge AI systems that leverage cloud intelligence without sacrificing latency requirements, thus significantly advancing both academic research and industry deployment in low-latency speech processing.
Technical Contribution
The main technical innovations include: • The design of a streaming TF-GridNet-based architecture capable of multi-task low-latency speech processing; • The introduction of a delayed hint mechanism, where embeddings generated by a large model are transmitted with delay and fused via cross-attention; • A joint training framework that encourages the large model to produce useful hints and the small model to utilize them effectively; • Embedding compression modules to reduce bandwidth requirements, enabling practical deployment over wireless links. These contributions collectively enable the first systematic approach to leveraging delayed remote hints for real-time speech tasks, offering a new paradigm for edge-cloud collaborative inference.
Novelty
This research is pioneering in systematically integrating delayed hints from remote large models into real-time, streaming speech processing pipelines. Unlike traditional knowledge distillation or model partitioning, which assume simultaneous access to current inputs, this approach explicitly accounts for communication delays, designing mechanisms for effective information fusion over time. The use of cross-attention modules to dynamically incorporate delayed embeddings into the small model's inference process is a novel contribution, addressing a previously overlooked challenge in low-latency model collaboration. This innovation opens new avenues for scalable, cloud-assisted edge AI, particularly in bandwidth-constrained or latency-sensitive scenarios.
Limitations
- The approach relies on stable, low-latency communication channels; significant network disruptions or high latency could impair performance or cause failure.
- The effectiveness diminishes with delays exceeding 50ms, limiting applicability in scenarios with poor connectivity or long-distance transmission.
- Training complexity is high due to joint optimization of large and small models, requiring substantial computational resources and careful hyperparameter tuning.
- Current implementation is tailored for audio tasks with specific chunk sizes; adapting to other modalities or variable latency conditions remains a challenge.
Future Work
Future research should focus on developing adaptive mechanisms that dynamically adjust delay compensation based on real-time network conditions. Exploring more efficient compression techniques, such as neural vocoders or quantization, can further reduce bandwidth demands. Extending the framework to multi-modal data, including visual cues, could enhance robustness and versatility. Additionally, investigating model robustness under network failures and designing fallback strategies will be crucial for real-world deployment. Long-term, integrating this approach into scalable cloud-edge architectures could revolutionize real-time intelligent systems across various domains.
AI Executive Summary
In the rapidly evolving landscape of intelligent audio processing, achieving high performance under strict low-latency constraints remains a significant challenge. Traditional large neural models, while capable of delivering state-of-the-art results in speech separation, enhancement, and extraction, are computationally prohibitive for deployment on resource-limited edge devices such as wearables, smartphones, or embedded systems. Conversely, small models designed for such devices often lack the capacity to match the performance of their larger counterparts. This dichotomy has driven research into model partitioning, knowledge distillation, and edge-cloud collaboration, but these methods face fundamental limitations when communication latency and real-time processing requirements are considered.
This paper introduces 'Knowledge Boosting,' a novel framework that leverages remote large models to provide delayed hints to local small models during inference. The core idea is to utilize the intermediate representations—embeddings—generated by the large model, transmit them with inherent communication delays, and fuse them into the small model's inference pipeline via cross-attention mechanisms. This approach allows the small model to benefit from the large model's knowledge without requiring the large model to operate in real-time locally, thus overcoming bandwidth and latency constraints.
The architecture employs a streaming neural network based on TF-GridNet, optimized for low-latency, multi-task speech processing. During training, the large model is pre-trained to produce stable embeddings, which are then compressed and stored. In inference, these embeddings are transmitted with delay, mimicking real-world wireless communication conditions such as Bluetooth or Wi-Fi. The small model, equipped with merge modules that incorporate cross-attention, dynamically integrates delayed hints with current input features. The joint training process ensures that the large model learns to generate useful hints, and the small model learns to utilize them effectively, even with delays up to 48ms.
Experimental results across three key tasks—speech enhancement, blind source separation, and target speech extraction—demonstrate the effectiveness of the method. Under a maximum delay of 48ms, the knowledge boosting approach yields improvements of 0.23dB, 2.31dB, and 3.53dB in SI-SDR, respectively, outperforming baseline models of similar size. These gains are achieved with approximately 50% reduction in computational complexity, making the approach suitable for deployment on resource-constrained devices. Ablation studies confirm that larger delays reduce performance, but the proposed fusion mechanism mitigates this effect, validating the robustness of the framework.
This research offers a transformative perspective on edge-cloud collaboration, enabling real-time, high-quality speech processing by intelligently leveraging remote model capabilities despite communication delays. Its implications extend beyond audio, promising advancements in multi-modal perception, autonomous systems, and IoT devices. While limitations such as dependency on stable network conditions and training complexity remain, the framework sets a foundation for future innovations in scalable, efficient, and intelligent edge AI systems, paving the way for smarter, more responsive devices in everyday life.
Deep Dive
Abstract
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 https://knowledgeboosting.cs.washington.edu/.
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