Sharif Digital Repository

Server workloads operate on large volumes of data. As a result, processors executing these workloads encounter frequent L1-D misses. In a many-core processor, an L1-D miss causes a request packet to be sent to an LLC slice and a response packet to be sent back to the L1-D, which results in high overhead. While prior work targeted response packets, this work focuses on accelerating the request packets. Unlike aggressive OoO cores, simpler cores used in many-core processors cannot hide the latency of L1-D request packets. We observe that LLC slices that serve L1-D misses are strongly temporally correlated. Taking advantage of this observation, we design a simple and accurate predictor. Upon... 

The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (Trg) and critical thickness (Dmax). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To... 

This paper presents two effective methods to reduce power consumption of branch target buffer (BTB): 1) the first method is based on storing distance to next branch address in tag array instead of storing whole branch address, 2) the second method is to use a new field in data array of BTB namely Next Branch Distance (NBD) which holds distance of next branch address from current branch address. When a new hit is performed in BTB, based on NBD field, there would be no access through NBD number of instructions, so BTB can be shutdown not to consume power. The new architecture does not impose extra delay and reduction in prediction accuracy. Both methods were implemented and simulated using... 

This paper presents two effective methods to reduce power consumption of branch target buffer (BTB): 1) the first method is based on storing distance to next branch address in tag array instead of storing whole branch address, 2) the second method is to use a new field in data array of BTB namely Next Branch Distance (NBD) which holds distance of next branch address from current branch address. When a new hit is performed in BTB, based on NBD field, there would be no access through NBD number of instructions, so BTB can be shutdown not to consume power. The new architecture does not impose extra delay and reduction in prediction accuracy. Both methods were implemented and simulated using... 

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

Inference and Estimation in Missing Information (MI) scenarios are important topics in Statistical Learning Theory and Machine Learning (ML). In ML literature, attempts have been made to enhance prediction through precise feature selection methods. In sparse linear models, LASSO is well-known in extracting the desired support of the signal and resisting against noisy systems. When sparse models are also suffering from MI, the sparse recovery and inference of the missing models are taken into account simultaneously. In this paper, we will introduce an approach which enjoys sparse regression and covariance matrix estimation to improve matrix completion accuracy, and as a result enhancing... 

Oil and gas are likely the most important sources for producing heat and energy in both domestic and industrial applications. Hydrocarbon reservoirs that contain these fuels are required to be characterized to exploit the maximum amount of their fluids. Well testing analysis is a valuable tool for the characterization of hydrocarbon reservoirs. Handling and analysis of long-term and noise-contaminated well testing signals using the traditional methods is a challenging task. Therefore, in this study, a novel paradigm that combines wavelet transform (WT) and recurrent neural networks (RNN) is proposed for analyzing the long-term well testing signals. The WT not only reduces the dimension of... 

We present a novel deep convolutional neural network (DCNN) model for predicting human behavior in repeated games. The model is the first deep neural network presented on repeated games that is able to be trained on games with arbitrary size of payoff matrices. Our neural network takes the players’ payoff matrices and the history of the play as input, and outputs the predicted action picked by the first player in the next round. To evaluate the model’s performance, we apply it to some experimental games played by humans and measure the rate of correctly predicted actions. The results show that our model obtains an average prediction accuracy of about 63% across all the studied games, which... 

Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting... 

Market prediction has been an important machine learning research topic in recent decades. A neglected issue in prediction is having a model that can simultaneously pay attention to the interaction of global markets along historical data of the target markets being predicted. As a solution, we present a hybrid supervised semi-supervised model called HyS3 for direction of movement prediction. The graph-based semi-supervised part of HyS3 models the markets global interactions through a network designed with a novel continuous Kruskal-based graph construction algorithm called ConKruG. The supervised part of the model injects results extracted from each market's historical data to the network... 

This study introduces a new identifier for nuclear power plants (NPPs) transients. The proposed identifier changes the representation of input patterns. Change of representation is a semi-supervised learning algorithm which employs both of labeled and unlabeled input data. In the first step, modified self-organizing map (MSOM) carries out an unsupervised learning algorithm on labeled and unlabeled patterns and generates a new metric for input data. In the second step, support vector machine (SVM) as a supervised learning algorithm classifies the input patterns using the generated metric of the first step. In contrast to unsupervised learning algorithms, the proposed identifier does not... 

Performance evaluation is an important engineering tool that provides valuable feedback on design choices in the implementation of multi-core systems such as parallel systems, multicomputers, and Systems-on-Chip (SoCs). The significant advantage of analytical models over simulation is that they can be used to obtain performance results for large systems under different configurations and working conditions which may not be feasible to study using simulation on conventional computers due to the excessive computation demands. We present Caspian, a novel analytic performance model, aimed to minimize prediction cost, while providing prediction accuracy. This is accomplished by using a G/G/1... 

Topology control algorithms in mobile ad-hoc networks aim to reduce the power consumption while keeping the topology connected. These algorithms can preserve network resources and increase network capacity. However, few efforts have focused on the issue of topology control in presence of node mobility. One of the notable mobility aware topology control protocols is the "Mobility Aware Distributed Topology Control Protocol". The main drawback of this protocol is on its mobility prediction method. This prediction method assumes linear movements and is unable to cope with sudden changes in the mobile node movements. In this paper, we propose a pattern matching based mobility prediction method... 

The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well design and operating procedures. However, certain rock formations and their fluid pressure and stress conditions are more prone to casing-collapse risks than others. The Gachsaran Formation in south west Iran, is one such formation, central to oil and gas resource exploration and development in the Zagros region and consisting of complex alternations of anhydrite, marl and salt. The casing-collapse incidents in this formation have resulted over decades in substantial lost production and remedial costs to mitigate the issues surrounding wells with failed casing string. High and vertically-varying... 

As benign tumors, warts are made through the mediation of Human Papillomavirus (HPV) and may grow on all parts of body, especially hands and feet. There are several treatment methods for this illness. However, none of them can heal all patients. Consequently, physicians are looking for more effective and customized treatments for each patient. They are endeavoring to discover which treatments have better impacts on a particular patient. The aim of this study is to identify the appropriate treatment for two common types of warts (plantar and common) and to predict the responses of two of the best methods (immunotherapy and cryotherapy) to the treatment. As an original work, the study was...