基于自适应速率深度面向任务的矢量量化的动态链路远程推断

IF 5.8 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Eyal Fishel;May Malka;Shai Ginzach;Nir Shlezinger
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引用次数: 0

摘要

广泛的技术依赖于远程推理,其中获取的数据通过通信通道传送,以便在远程服务器中进行推理。参与实体之间的通信通常在速率有限的通道上进行,因此需要对数据进行压缩以减少延迟。虽然深度学习促进了压缩映射与编码和推理规则的联合设计,但现有的学习压缩机制是静态的,并且难以使其分辨率适应信道条件和动态链接的变化。为了解决这个问题,我们提出了自适应速率任务导向矢量量化(ARTOVeQ),这是一种为动态链接上的远程推理量身定制的学习压缩机制。ARTOVeQ基于设计嵌套码本以及采用渐进式学习的学习算法。我们展示了ARTOVeQ扩展到支持低延迟推理,通过连续的改进原则逐步改进,并且在传输高维数据时能够同时使用多种分辨率。数值结果表明,该方案能够实现多速率的远程深度推理,支持大范围的比特预算,并且能够实现快速推理,随着比特交换量的增加,推理能力会逐渐提高,同时性能接近单速率深度量化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Remote Inference Over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
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.
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来源期刊
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing 工程技术-工程:电子与电气
CiteScore
11.20
自引率
9.30%
发文量
310
审稿时长
3.0 months
期刊介绍: The IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals. The term “signal” includes, among others, audio, video, speech, image, communication, geophysical, sonar, radar, medical and musical signals. Examples of topics of interest include, but are not limited to, information processing and the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals.
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