預測深度學習模型於加速裝置上之執行時間

Abstract

隨著深度學習的迅速發展，而為了提高執行的效率，運作深度學習的硬體也 是愈發重要，但效能較高的平臺往往也伴隨著高昂的價格，因此本研究的目標便在於讓使用者能夠快速的推算出一個系統的效能，甚至是可以在取得目標硬體之前便可對其效能做簡易的分析。 目前也有許多相關的研究，但其多是使用公式的方式作效能預測，而此方式 往往使用線性的方式做計算，如此便會忽略許多細節。而本研究所用的方式為收集足夠多相關資料，並使用深度學習的方式去學習不同的配置下的執行時間。本研究同時也對不同的神經網路做預測，甚至是未取得的硬體。

With the rapid development of deep learning, in order to improve the efficiency of implementation, the hardware for deep learning is becoming more and more important, but the platform with higher performance is often accompanied by high prices. Therefore, the goal of this research is to let users can quickly calculate the performance of a system, and even can easily analyze its performance before getting the target hardware. There are a lot of related researches at present, but most of them use formulas to make performance predictions, and this method often uses linear methods to do calculations, so many details are ignored. The method used in this study is to collect enough relevant data and use deep learning to learn the computation time under different configurations. This study also predicts different neural networks, even for unavailable hardware.