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arXiv · 1908.11450

Neural Network Inference on Mobile SoCs

Siqi Wang·Anuj Pathania·Tulika Mitra
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Abstract

The ever-increasing demand from mobile Machine Learning (ML) applications calls for evermore powerful on-chip computing resources. Mobile devices are empowered with heterogeneous multi-processor Systems-on-Chips (SoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference. Mobile SoCs house several different types of ML capable components on-die, such as CPU, GPU, and accelerators. These different components are capable of independently performing inference but with very different power-performance characteristics. In this article, we provide a quantitative evaluation of the inference capabilities of the different components on mobile SoCs. We also present insights behind their respective power-performance behavior. Finally, we explore the performance limit of the mobile SoCs by synergistically engaging all the components concurrently. We observe that a mobile SoC provides up to 2x improvement with parallel inference when all its components are engaged, as opposed to engaging only one component.

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Siqi Wang, Anuj Pathania, Tulika Mitra. 2019-08-24. Neural Network Inference on Mobile SoCs. https://doi.org/10.1109/mdat.2020.2968258

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