• Journal of the Korea Society of Computer and Information
• /
• v.16 no.7
• /
• pp.1-11
• /
• 2011

Many researchers have studied on the methods to improve the processor performance. However, high integrated semiconductor technology for improving the processor performance causes many problems such as battery life, high power density, hotspot, etc. Especially, as hotspot has critical impact on the reliability of chip, thermal problems should be considered together with performance and power consumption when designing high-performance processors. To alleviate the thermal problems of processors, there have been various researches. In the past, mechanical cooling methods have been used to control the temperature of processors. However, up-to-date microprocessors causes severe thermal problems, resulting in increased cooling cost. Therefore, recent studies have focused on architecture-level thermal-aware design techniques than mechanical cooling methods. Even though architecture-level thermal-aware design techniques are efficient for reducing the temperature of processors, they cause performance degradation inevitably. Therefore, if the mechanical cooling methods can manage the thermal problems of processors efficiently, the performance can be improved by reducing the performance degradation due to architecture-level thermal-aware design techniques such as dynamic thermal management. In this paper, we analyze the cooling efficiency of high-performance multicore processors according to mechanical cooling methods. According to our experiments using air cooler and liquid cooler, the liquid cooler consumes more power than the air cooler whereas it reduces the temperature more efficiently. Especially, the cost for reducing $1^{\circ}C$ is varied by the environments. Therefore, if the mechanical cooling methods can be used appropriately, the temperature of high-performance processors can be managed more efficiently.

• Korean Chemical Engineering Research
• /
• v.61 no.4
• /
• pp.523-541
• /
• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.