Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
This work addresses the need for adaptive compression in remote inference systems, which is incremental as it builds on existing deep learning-based compression methods by adding dynamic adaptation capabilities.
The paper tackled the problem of remote inference over dynamic, rate-limited channels by proposing ARTOVeQ, a learned compression mechanism that adapts to changing channel conditions, resulting in performance approaching single-rate deep quantization methods while supporting multiple rates and rapid, gradually improving inference.
A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the participating entities is often carried out over rate-limited channels, necessitating data compression for reducing latency. While deep learning facilitates joint design of the compression mapping along with encoding and inference rules, existing learned compression mechanisms are static, and struggle in adapting their resolution to changes in channel conditions and to dynamic links. To address this, we propose Adaptive Rate Task-Oriented Vector Quantization (ARTOVeQ), a learned compression mechanism that is tailored for remote inference over dynamic links. ARTOVeQ is based on designing nested codebooks along with a learning algorithm employing progressive learning. We show that ARTOVeQ extends to support low-latency inference that is gradually refined via successive refinement principles, and that it enables the simultaneous usage of multiple resolutions when conveying high-dimensional data. Numerical results demonstrate that the proposed scheme yields remote deep inference that operates with multiple rates, supports a broad range of bit budgets, and facilitates rapid inference that gradually improves with more bits exchanged, while approaching the performance of single-rate deep quantization methods.