【摘要】：近年來,以神經網絡為代表的機器學習算法發(fā)展迅速并被廣泛應用在圖像識別、數據搜索乃至金融趨勢分析等領域.而隨著問題規(guī)模的擴大和數據維度的增長,算法能耗問題日益突出,由于機器學習算法自身擁有的近似特性,近似計算這種犧牲結果的少量精確度降低能耗的技術,被許多研究者用來解決學習算法的能耗問題.我們發(fā)現(xiàn),目前的工作大多專注于利用特定算法的近似特性而忽視了不同算法近似特性的差別對能耗優(yōu)化帶來的影響,而為了分類任務使用近似計算時能夠做出能耗最優(yōu)的選擇,了解算法"可近似性"上的差異對近似計算優(yōu)化能耗至關重要.因此,選取了支持向量機(SVM)、隨機森林(RF)和神經網絡(NN)3類常用的監(jiān)督型機器學習算法,評估了針對不同類型能耗時不同算法的可近似性,并建立了存儲污染敏感度、訪存污染敏感度和能耗差異度等指標來表征算法可近似性的差距,評估得到的結論將有助于機器學習算法在使用近似計算技術時達到最優(yōu)化能耗的目的.
[Abstract]:In recent years, machine learning algorithms, represented by neural networks, have developed rapidly and have been widely used in image recognition, data search and even financial trend analysis. With the expansion of the scale of the problem and the increase of the data dimension, the problem of energy consumption of the algorithm is becoming more and more prominent. Because of the approximate characteristics of the machine learning algorithm itself, the technology of reducing energy consumption by approximate calculation of a small amount of accuracy of the sacrifice results has been used by many researchers to solve the problem of energy consumption of the learning algorithm. We find that most of the current work focuses on using the approximate characteristics of specific algorithms and neglects the influence of the difference of approximate characteristics of different algorithms on energy consumption optimization. In order to make the optimal choice of energy consumption when using approximate calculation of classification tasks, it is very important to understand the difference of algorithm "approximability" for approximate calculation of optimal energy consumption. Therefore, three kinds of supervised machine learning algorithms, support vector machine (SVM), random forest (RF) and neural network (NN), are selected to evaluate the approximability of different algorithms for different types of energy consumption, and some indexes, such as storage pollution sensitivity, visiting pollution sensitivity and energy consumption difference, are established to characterize the similarity gap of the algorithm. The conclusion of the evaluation will help the machine learning algorithm to optimize the energy consumption when using approximate computing technology.
【作者單位】： 計算機體系結構國家重點實驗室(中國科學院計算技術研究所);中國科學院大學;
【基金】：國家自然科學基金項目(61572470,61532017,61522406,61432017,61376043,61521092) 中國科學院青年創(chuàng)新促進會項目(404441000)~~
【分類號】：TP181
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本文編號：2504799