Towards a learning-based performance modeling for accelerating Deep Neural Networks

• URL: http://arxiv.org/abs/2212.05031v1
• Date: Fri, 9 Dec 2022 18:28:07 GMT
• Title: Towards a learning-based performance modeling for accelerating Deep Neural Networks
• Authors: Damiano Perri, Paolo Sylos Labini, Osvaldo Gervasi, Sergio Tasso, Flavio Vella
• Abstract summary: We start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs) Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.
• Score: 1.1549572298362785
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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