Asynchronous Pipeline Parallelism for Real-Time Multilingual Lip Synchronization in Video Communication Systems
2025 IEEE International Conference on Big Data, BigData 2025, Macau, Çin, 8 - 11 Aralık 2025, ss.7012-7020, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/bigdata66926.2025.11400775
- Basıldığı Şehir: Macau
- Basıldığı Ülke: Çin
- Sayfa Sayıları: ss.7012-7020
- Anahtar Kelimeler: AIoT communication, inference optimization, message-oriented middleware, multi-modal translation, pipeline parallelism, real-time systems, resource efficiency, video conferencing
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
This paper introduces a parallel and asynchronous Transformer framework designed for efficient and accurate multilingual lip synchronization in real-time video conferencing systems. The proposed architecture integrates translation, speech processing, and lip-synchronization modules within a pipeline-parallel design that enables concurrent module execution through message-queue-based decoupling, reducing end-to-end latency by up to 3.1x compared to sequential approaches. To enhance computational efficiency and throughput, the inference workflow of each module is optimized through low-level graph compilation, mixed-precision quantization, and hardware-accelerated kernel fusion. These optimizations provide substantial gains in efficiency while preserving model accuracy and visual quality. In addition, a context-adaptive silence-detection component segments the input speech stream at semantically coherent boundaries, improving translation consistency and temporal alignment across languages. Experimental results demonstrate that the proposed parallel architecture outperforms conventional sequential pipelines in processing speed, synchronization stability, and resource utilization. The modular, message-oriented design makes this work applicable to resource-constrained IoT communication scenarios including telemedicine, multilingual kiosks, and remote assistance systems. Overall, this work advances the development of low-latency, resource-efficient multimodal communication frame-works for next-generation AIoT systems.