# Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

## TL;DR

This paper develops and evaluates two performance models to estimate training times of convolutional neural networks on Intel Xeon Phi architectures, achieving prediction accuracies of 15% and 11%.

## Contribution

It introduces two parameterized performance models for deep learning training on Intel many integrated core architectures, with differing measurement approaches.

## Key findings

- First model achieves 15% prediction accuracy.
- Second model achieves 11% prediction accuracy.
- Models effectively estimate training times for CNNs.

## Abstract

Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture. While for the first performance model we minimally use measurement techniques for parameter value estimation, in the second model we estimate more parameters based on measurements. We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi. The achieved average performance prediction accuracy is about 15% for the first model and 11% for second model.