Empirical architecture comparison of two-input machine learning systems for vision tasks

Abstract

As machine learning models have been deployed in many vision systems including autonomous vehicles and robots, designing architectures for machine learning systems (MLSs) has emerged as a critical concern. Previous studies have shown that enhancing the reliability of MLS outputs can be achieved by comparing multiple inference results on distinct inputs. Nevertheless, the architectures facilitating multiple inferences incur non-negligible performance overhead and energy consumption that have been less investigated. This paper delves into the trade-offs among reliability, performance, and energy efficiency of architectures for two-input MLSs through real experiments conducted on image classification and object detection tasks. Specifically, we scrutinize the comparison between parallel and shared-type architectures of two-input MLSs for vision tasks. The experiments confirm that the shared type architecture can achieve a shorter response time and smaller energy consumption by using a shared machine learning module for both image classification and object detection tasks. On the other hand, the parallel type architecture can benefit the redundant machine learning modules for improving throughput and fault tolerance. Our empirical results also show the service time distributions of image classification and object detection tasks fit well with a log-normal distribution and a mixture of the Gaussian model, respectively.