AdaFlow: Opportunistic Inference on Asynchronous Mobile Data with Generalized Affinity Control

TL;DR

AdaFlow introduces a novel opportunistic inference framework for asynchronous multi-modal mobile data, leveraging structured affinity control and adaptive data imputation to reduce latency and improve accuracy in dynamic environments.

Contribution

It pioneers a structured affinity control method using hierarchical analysis and an affinity attention-based GAN for flexible, real-time multi-modal data inference without retraining.

Findings

Reduces inference latency by up to 79.9%.

Improves accuracy by up to 61.9%.

Outperforms existing approaches significantly.

Abstract

The rise of mobile devices equipped with numerous sensors, such as LiDAR and cameras, has spurred the adoption of multi-modal deep intelligence for distributed sensing tasks, such as smart cabins and driving assistance. However, the arrival times of mobile sensory data vary due to modality size and network dynamics, which can lead to delays (if waiting for slower data) or accuracy decline (if inference proceeds without waiting). Moreover, the diversity and dynamic nature of mobile systems exacerbate this challenge. In response, we present a shift to \textit{opportunistic} inference for asynchronous distributed multi-modal data, enabling inference as soon as partial data arrives. While existing methods focus on optimizing modality consistency and complementarity, known as modal affinity, they lack a \textit{computational} approach to control this affinity in open-world mobile environments. AdaFlow pioneers the formulation of structured cross-modality affinity in mobile contexts using a hierarchical analysis-based normalized matrix. This approach accommodates the diversity and dynamics of modalities, generalizing across different types and numbers of inputs. Employing an affinity attention-based conditional GAN (ACGAN), AdaFlow facilitates flexible data imputation, adapting to various modalities and downstream tasks without retraining. Experiments show that AdaFlow significantly reduces inference latency by up to 79.9\% and enhances accuracy by up to 61.9\%, outperforming status quo approaches.