arXiv:2402.14326v1 Announce Type: new
Abstract: Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as video semantic segmentation (VSS), partly due to the fluctuating VSS accuracy and segment bitrate caused by the dynamic video content. In response, we present Penance, a new edge inference cost reduction framework. By exploiting softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs, Penance optimizes model selection and compression settings to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. We implement Penance in a commercial IoT device with only CPUs. Experimental results show that Penance consumes a negligible 6.8% more computation resources than the optimal strategy while satisfying accuracy and bandwidth constraints with a low failure rate.
Analysis of Penance: Edge Inference Cost Reduction Framework
In this article, the authors introduce Penance, a new framework for reducing edge inference costs in video semantic segmentation (VSS) tasks. With the growing demand for video understanding applications on resource-constrained IoT devices, offloading computing to edge servers has become a promising solution. However, existing research is not directly applicable to pixel-level vision tasks like VSS, mainly due to the dynamic nature of video content, which leads to fluctuating accuracy and segment bitrate.
Penance addresses this challenge by leveraging the softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs. By optimizing model selection and compression settings, Penance aims to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. It is worth noting that Penance is implemented on a commercial IoT device with only CPUs, making it accessible to a wide range of devices.
The multi-disciplinary nature of this work is evident in its integration of computer vision (specifically VSS), video codecs (H.264/AVC), and edge computing. It combines knowledge from these diverse domains to develop a novel solution that addresses the specific challenges faced in edge inference for VSS.
When considering the wider field of multimedia information systems, Penance contributes to the efficiency and scalability of video understanding applications on IoT devices. By reducing inference costs at the edge, it enables resource-constrained devices to perform complex vision tasks like semantic segmentation without relying heavily on cloud resources. This can lead to improved response times, reduced latency, and increased privacy.
Furthermore, Penance has relevance to various aspects of multimedia technologies such as animations, artificial reality, augmented reality, and virtual realities. These technologies often involve real-time video processing and analysis, where efficient edge inference is crucial for a seamless and immersive user experience. By optimizing inference costs, Penance can support the delivery of rich multimedia content in these applications without compromising on performance.
In conclusion, Penance is an innovative framework that addresses the challenges of edge inference for video semantic segmentation tasks. Its integration of various technologies and its impact on the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities make it a significant contribution to the advancement of edge computing in the context of video understanding applications.