LAUSR

Recently, deep learning has brought revolutions to many mobile and embedded systems that interact with the physical world using continuous video streams. Although there have been significant efforts to reduce the computational overheads of deep learning inference in such systems, previous approaches have focused on delivering ‘best-effort’ performance, resulting in unpredictable performance under variable environments. In this paper, we propose a runtime control method, called DMS (Dynamic Model Scaling), that enables dynamic resource-accuracy trade-offs to support various QoS requirements of deep learning applications. In DMS, the resource demands of deep learning inference can be controlled by adaptive pruning of computation-intensive convolution filters. DMS avoids irregularity of pruned models by reorganizing filters according to their importance so that varying number of filters can be applied efficiently. Since DMS’s pruning method incurs no runtime overhead and preserves the full capacity of original deep learning models, DMS can tailor the models at runtime for concurrent deep learning applications with their respective resource-accuracy trade-offs. We demonstrate the viability of DMS by implementing a prototype. The evaluation results demonstrate that, if properly coordinated with system level resource managers, DMS can support highly robust and efficient inference performance against unpredictable workloads.