Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.

Towards a Learning-Based Performance Modeling for Accelerating Deep Neural Networks / Perri, D.; Sylos Labini, P.; Gervasi, O.; Tasso, S.; Vella, F.. - ELETTRONICO. - 11619:(2019), pp. 665-676. (Intervento presentato al convegno 19th International Conference on Computational Science and Its Applications, ICCSA 2019 tenutosi a Russia nel July 1–4, 2019) [10.1007/978-3-030-24289-3_49].

Towards a Learning-Based Performance Modeling for Accelerating Deep Neural Networks

Vella F.
2019-01-01

Abstract

Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.
2019
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Saint Petersburg, Russia
Springer Verlag
978-3-030-24288-6
978-3-030-24289-3
Perri, D.; Sylos Labini, P.; Gervasi, O.; Tasso, S.; Vella, F.
Towards a Learning-Based Performance Modeling for Accelerating Deep Neural Networks / Perri, D.; Sylos Labini, P.; Gervasi, O.; Tasso, S.; Vella, F.. - ELETTRONICO. - 11619:(2019), pp. 665-676. (Intervento presentato al convegno 19th International Conference on Computational Science and Its Applications, ICCSA 2019 tenutosi a Russia nel July 1–4, 2019) [10.1007/978-3-030-24289-3_49].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/332695
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