Neural Network Inference on Mobile SoCs
| Autor: | Anuj Pathania, Tulika Mitra, Siqi Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Multi-core processor Artificial neural network Computer science Inference Machine Learning (stat.ML) Machine Learning (cs.LG) Computer Science - Distributed Parallel and Cluster Computing Computer architecture Statistics - Machine Learning Hardware and Architecture Distributed Parallel and Cluster Computing (cs.DC) State (computer science) Electrical and Electronic Engineering Mobile device Throughput (business) Software |
| Zdroj: | IEEE Design & Test. 37:50-57 |
| ISSN: | 2168-2364 2168-2356 |
| Popis: | 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. Comment: Accepted to IEEE Design & Test |
| Databáze: | OpenAIRE |
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