Distributed Edge Inference: an Experimental Study on Multiview Detection

2026-09-17

AI Summary

研究通过分布式边缘推理解决云计算中应用扩展性受限问题，使用FastFL框架实现多视角检测模型MvDet，并在多种实验场景下测试其性能。

Abstract

Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum offers to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.

Problem

• Distributed Edge Inference
• Compute Continuum
• Scalability

Innovation

• Distributed Edge Inference
• Compute Continuum
• Multi-view Detection
• FastFL Framework