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arXiv · 2511.16225

Real-Time Inference for Distributed Multimodal Systems under Communication Delay Uncertainty

Victor Croisfelt·João Henrique Inacio de Souza·Shashi Raj Pandey·Beatriz Soret·Petar Popovski
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Abstract

Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams. Uncertain communication delays across data streams challenge the temporal flow of the inference process. State-of-the-art (SotA) non-blocking inference methods rely on a reference-modality paradigm, requiring one modality input to be fully received before processing, while depending on costly offline profiling. We propose a novel, neuro-inspired non-blocking inference paradigm that primarily employs adaptive temporal windows of integration (TWIs) to dynamically adjust to stochastic delay patterns across heterogeneous streams while relaxing the reference-modality requirement. Our communication-delay-aware framework achieves robust real-time inference with finer-grained control over the accuracy-latency tradeoff. Experiments on the audio-visual event localization (AVEL) task demonstrate superior adaptability to network dynamics compared to SotA approaches.

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Victor Croisfelt, João Henrique Inacio de Souza, Shashi Raj Pandey, Beatriz Soret, Petar Popovski. 2025-11-20. Real-Time Inference for Distributed Multimodal Systems under Communication Delay Uncertainty. https://arxiv.org/abs/2511.16225

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