Deep Reinforcement Learning based Online Scheduling Policy for Deep Neural Network Multi-Tenant Multi-Accelerator Systems
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| Publicado en: | arXiv.org (Apr 13, 2024), p. n/a |
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| Otros Autores: | , , , , |
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Cornell University Library, arXiv.org
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| Acceso en línea: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 3039629878 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3039629878 | ||
| 045 | 0 | |b d20240413 | |
| 100 | 1 | |a Blanco, Francesco G | |
| 245 | 1 | |a Deep Reinforcement Learning based Online Scheduling Policy for Deep Neural Network Multi-Tenant Multi-Accelerator Systems | |
| 260 | |b Cornell University Library, arXiv.org |c Apr 13, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assumes paramount importance, all while endeavoring to maintain cost-effectiveness. In this context, the utilization of heterogeneous multi-accelerator systems becomes increasingly relevant. This paper presents RELMAS, a low-overhead deep reinforcement learning algorithm designed for the online scheduling of DNNs in multi-tenant environments, taking into account the dataflow heterogeneity of accelerators and memory bandwidths contentions. By doing so, service providers can employ the most efficient scheduling policy for user requests, optimizing Service-Level-Agreement (SLA) satisfaction rates and enhancing hardware utilization. The application of RELMAS to a heterogeneous multi-accelerator system composed of various instances of Simba and Eyeriss sub-accelerators resulted in up to a 173% improvement in SLA satisfaction rate compared to state-of-the-art scheduling techniques across different workload scenarios, with less than a 1.5% energy overhead. | |
| 653 | |a Scheduling | ||
| 653 | |a Algorithms | ||
| 653 | |a Deep learning | ||
| 653 | |a System effectiveness | ||
| 653 | |a Computer aided scheduling | ||
| 653 | |a Machine learning | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Accelerators | ||
| 653 | |a Heterogeneity | ||
| 700 | 1 | |a Russo, Enrico | |
| 700 | 1 | |a Palesi, Maurizio | |
| 700 | 1 | |a Patti, Davide | |
| 700 | 1 | |a Ascia, Giuseppe | |
| 700 | 1 | |a Catania, Vincenzo | |
| 773 | 0 | |t arXiv.org |g (Apr 13, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3039629878/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2404.08950 |