On-chip mechanisms to reduce effective memory access latency

This dissertation develops hardware that automatically reduces the effective latency of accessing memory in both single-core and multi-core systems. To accomplish this, the dissertation shows that all last level cache misses can be separated into two categories: dependent cache misses and independent cache misses. Independent cache misses have all of the source data that is required to generate the address of the memory access available on-chip, while dependent cache misses depend on data that is located off-chip. This dissertation proposes that dependent cache misses are accelerated by migrating the dependence chain that generates the address of the memory access to the memory controller for execution. Independent cache misses are accelerated using a new mode for runahead execution that only executes filtered dependence chains. With these mechanisms, this dissertation demonstrates a 62% increase in performance and a 19% decrease in effective memory access latency for a quad-core processor on a set of high memory intensity workloads.Electrical and Computer Engineerin

Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks

In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where the goal is to learn an algorithm (e.g., sorting, breadth-first search, and depth-first search) from input-output pairs using deep neural networks. First, we argue that OOD generalization in this setting is significantly different than common OOD settings. For example, some phenomena in OOD generalization of image classifications such as \emph{accuracy on the line} are not observed here, and techniques such as data augmentation methods do not help as assumptions underlying many augmentation techniques are often violated. Second, we analyze the main challenges (e.g., input distribution shift, non-representative data generation, and uninformative validation metrics) of the current leading benchmark, i.e., CLRS \citep{deepmind2021clrs}, which contains 30 algorithmic reasoning tasks. We propose several solutions, including a simple-yet-effective fix to the input distribution shift and improved data generation. Finally, we propose an attention-based 2WL-graph neural network (GNN) processor which complements message-passing GNNs so their combination outperforms the state-of-the-art model by a 3% margin averaged over all algorithms. Our code is available at: \url{https://github.com/smahdavi4/clrs}

Electric Field Induced Alignment of Carbon Nanotubes: Methodology and Outcomes

In the current chapter, achievement of aligned carbon nanotube (CNT) network within the matrix via various kinds of electric fields (AC and DC) was evaluated. In this case, alignment mechanism of CNTs within the matrix and two useful techniques for justification of CNT alignment throughout the matrix were examined and presented, respectively. Afterward, effective factors in matter of CNT alignment and applicable procedures for fabrication of nanocomposites containing aligned CNTs were studied and presented, respectively. At the end, significant effects of CNT alignment on overall properties of nanocomposites that include electrical and mechanical properties were evaluated. Achieved results revealed that alignment of CNTs within the matrix can lead to significant improvement in the electrical and mechanical properties of nanocomposites at the same filler loading compared with randomly distribution of CNTs within the matrix, while production steps and conditions can also highly affect the outcome data

FREQUENCY AND PREDICTOR FACTORS OF IRREVERSIBLE PULPITIS

Abstract. Objective: Referral pains are one of the most common challenges which dentists are faced with them during diagnosis and before treatment. Pain referral can take place in tooth and other craniofacial structures and influence the diagnostic process. The present study was accomplished to evaluate the prevalence of irreversible pulpitis in patients refferd to Endodontic department of dental school of shahid Sadoughi University in 2017 with chief complaint of pain. Methods: This study is a descriptive and cross-sectional type of study conducted on 100 patients(21 males and 79 females) refferd to Endodontic department of dental school of shahid Sadoughi University in 2017 with chief complaint of pain.Informed consent was taken from patients. Data obtained from medical history, dentistry history, clinical examinations, and radiography were recorded in questionnaire developed for this purpose. Finally, the data were analyzed using SPSS18 software, Chi-square and Fisher exact test. Results: In the present research, the prevalence of irreversible pulpitis was obtained to be 77%. This prevalence in female was significantly more than males (P-value = 0.021). Patients with irreversible pulpit significantly reported more severe pain (p-value=0.000) and pain at the real site (p-value=0.028).The frequency of irreversible pulpitis showed no significant correlation with age and type of pain (P-value<0.05). Conclusion: Considering the findings of this research, the prevalence of irreversible pulpitis in patients refferd to Endodontic department of dental school of shahid Sadoughi University in 2017 with chief complaint of pain was three times more than that of other diseases. This frequency showed significant relationship with factors of gender, pain severity, and the pain feeling site.Keywords: Irreversible pulpitis, pain, root canal treatment

Optimized Selection of Stock Portfolio by using the Fuzzy Artificial Neural Networks Web Model, ARIMA & Markowitz Model in Tehran Stock Exchange

Financial issues have always been the main topics of scholars’ research. Fuzzy logic is one of the techniques that are widely used in this study in order to model the environmental uncertainty. The aim of this paper is combination of fuzzy logic and neural networks to select a basket (portfolio) of stocks. Web forecasting system based on fuzzy artificial neural networks that discovers fuzzy rules by using the past time data and predicts it, is also applied in this learning algorithm. The data of this study have been collected weekly from the Tehran Stock Exchange. Output data Simulation had been collected from the stock market base using obtained output data. This paper first deals with the study of financial markets. After that the research models were described and by using the other linear techniques such as Markowitz and ARIMA models, stock price was predicted. Then performance of the models was investigated with two population mean test (t-student) at the 95% confidence level. At the end Fuzzy artificial Neural Web network was selected as the best model for decision- makers. To perform research models and analysis of Java source code and software PASW 18 and ISP Server and also JDK3.1 were used. Finally, practical suggestions were given