XFeatur: Hardware Feature Extraction for DNN Auto-tuning
| Autor: | Sierra Acosta, Jorge, Diavastos, Andreas, González Colás, Antonio María |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors, Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors, Universitat Politècnica de Catalunya. ARCO - Microarquitectura i Compiladors |
| Rok vydání: | 2022 |
| Předmět: |
Artificial intelligence
Memory management (Computer science) Parallel processing (Electronic computers) Processament en paral·lel (Ordinadors) Machine learning TVM Aprenentatge automàtic Autotuning Gestió de memòria (Informàtica) Informàtica::Arquitectura de computadors::Arquitectures paral·leles [Àrees temàtiques de la UPC] |
| Zdroj: | 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software |
| Popis: | In this work, we extend the auto-tuning process of the state-of-the-art TVM framework with XFeatur; a tool that extracts new meaningful hardware-related features that improve the quality of the representation of the search space and consequently improve the accuracy of its prediction algorithm. These new features provide information about the amount of thread-level parallelism, shared memory usage, register usage, dynamic instruction count and memory access dependencies. Optimizing ResNet-18 with the proposed features improves the quality of the search space representation by 63% on average and a maximum of 2× for certain tasks, while it reduces the tuning time by 9% (approximately 1.1 hours) and produces configurations that have equal or better performance (up to 92.7%) than the baseline. This work has been supported by the CoCoUnit ERC Advanced Grant of the EU’s Horizon 2020 program (grant No 833057), the Spanish State Research Agency (MCIN/AEI) under grant PID2020-113172RB-I00, and the ICREA Academia program and the FPU grant 2019-FPU-998758. |
| Databáze: | OpenAIRE |
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