DeepSlicing: Collaborative and Adaptive CNN Inference With Low Latency
Shuai Zhang, Sheng Zhang, Zhuzhong Qian, Jie Wu, Yibo Jin, Sanglu Lu
Abstract
The booming of Convolutional Neural Networks (CNNs) has empowered lots of computer-vision applications. Due to its stringent requirement for computing resources, substantial research has been conducted on how to optimize its deployment and execution on resource-constrained devices. However, previous works have several weaknesses, including limited support for various CNN structures, fixed scheduling strategies, overlapped computations, high synchronization overheads, etc. In this article, we present DeepSlicing, a collaborative and adaptive inference system that adapts to various CNNs and supports customized flexible fine-grained scheduling. As a built-in functionality, DeepSlicing has supported typical CNNs including GoogLeNet, ResNet, etc. By partitioning both model and data, we also design an efficient scheduler, Proportional Synchronized Scheduler (PSS), which achieves the trade-off between computation and synchronization. Based on PyTorch, we have implemented DeepSlicing on the testbed with real-world edge settings that consists of 8 heterogeneous Raspberry Pi's. The results indicate that DeepSlicing with PSS outperforms the existing systems dramatically, e.g., the inference latency and memory footprint are reduced up to 5.79× and 14.72×, respectively.