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DualPIM: A Dual-Precision and Low-Power CNN Inference Engine Using SRAM- and eDRAM-based Processing-in-Memory Arrays 4

DualPIM: A Dual-Precision and Low-Power CNN Inference Engine...

引用

IEEE international conference on Artificial Intelligence Circuits and Systems (AICAS) - Intelligent Technology in the Post-Pandemic Era

作者： Jung, Sangwoo Lee, Jaehyun Noh, Huiseong Yoon, Jong-Hyeok Kung, Jaeha DGIST Dept EECS Daegu South Korea

ISBN: (数字)9781665409964

ISBN: (纸本)9781665409964

Recently, machine learning community has focused on developing deep learning models that are not only accurate but also efficient to deploy them on resource-limited devices. One popular approach to improve the model efficiency is to aggressively quantize both features and weight parameters. However, the quantization generally entails accuracy degradation thus additional compensation techniques are required. In this work, we present a novel network architecture, named DualNet, that leverages two separate bit-precision paths to effectively achieve high accuracy and low model complexity. On top of this new network architecture, we propose to utilize both SRAM-and eDRAM-based processing-in-memory (PIM) arrays, named DualPIM, to run each computing path in a DualNet at a dedicated PIM array. As a result, the proposed DualNet significantly reduces the energy consumption by 81% on average compared to other quantized neural networks (i.e., 4-bit and ternary), while achieving 13% higher accuracy on average.

关键词： convolutional neural networks deep learning processing-in-memory quantized neural networks

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Detecting SDCs in GPGPUs Through an Efficient Instruction Duplication Mechanism 14th

Detecting SDCs in GPGPUs Through an Efficient Instruction Du...

引用

14th international conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Wei, Xiaohui Jiang, Nan Wang, Xiaonan Yue, Hengshan Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China

ISBN: (纸本)9783030821531;9783030821524

As General-Purpose Graphics Processing Units (GPGPUs) are widely used in High-Performance computing (HPC) applications, the vulnerability of GPGPUs to soft errors becomes a critical concern. In this paper, we propose an efficient instruction duplication mechanism that merely duplicates SDC vulnerable instructions for reliability overhead saving. We first observe that the SDC proneness of individual instruction is related to its instruction type, fault propagation, and whether it affects shared memory. Then, leveraging these observed factors, we utilize machine learning to intelligently identify all the SDC vulnerable instructions of GPU applications and efficiently protect them. Experimental results show that our method achieves a 90.45% SDC coverage only duplicating 37.8% of static instructions, which achieves a significant improvement in terms of performance and SDC detection capability compared to the state-of-the-art duplication technique in GPUs.

关键词： GPGPUs Silent Data Corruptions (SDCs) Instruction duplication soft error machine learning Parallel computing

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A Survey: machine learning and Deep learning in Wheat Disease Detection and Classification

A Survey: Machine Learning and Deep Learning in Wheat Diseas...

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international conference on ent computing and Control Systems (ICICCS)

作者： Nitin Sahu Aruna Bhat Department of Computer Science & Engineering Delhi Technological University Delhi India

Cereals are a significant and vital source of food for humans. To feed the world’s expanding population, farmers must produce more crops. Plant diseases, however, have an impact on crop production and food quality. Wheat is among the most important crops consumed worldwide. Since Wheat Disease causes production loss, early disease detection and classification are absolutely essential. This research study has conducted a literature survey on studies published from the year 2017 to 2022 and summarized the three main types of Wheat Disease (fungal, bacterial, and insects), Wheat Disease datasets, and the current state of the art from the last six years of research. This research study has examined 24 studies on disease identification and classification by using various machine learning (ML) and Deep learning (DL) algorithms. The proposed research analysis shows that the majority of the literature on Wheat Disease focused on fungal disease, and the majority of the datasets used were self-acquired. Many State-of-the-art models have already produced excellent results, and many more need to be developed.

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Tropical Cyclone Analysis and Accumulated Precipitation Predictive Model Using Regression machine learning Algorithm 3rd

Tropical Cyclone Analysis and Accumulated Precipitation Pred...

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3rd international conference on Computer and Communication Engineering, CCCE 2023

作者： Abalos, Maribel S. Fajardo, Arnel C. College of Engineering Architecture and Technology Isabela State University Isabela Ilagan Philippines College of Computing Studies Information and Communication Technology Isabela State University Isabela Cauyan City Philippines

ISBN: (纸本)9783031352980

The Philippines is susceptible to tropical cyclones due to its geographical location which generally produces heavy rains and flooding of a large area that result in heavy casualties to human life and destruction of crops and properties. In the past 4 years, the Cagayan Valley region is hit by almost 20 Tropical Cyclones with different strengths and attributes. It is of utmost importance to have sufficient knowledge of such maritime phenomena for beneficial purposes. This study analyzed the Tropical Cyclones that hit Cagayan Valley using Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) dataset from 2017–2021. Different attributes were considered to create a framework, statistical analysis, and comparison matrix. Also, the dataset is analyzed using four Regression machine learning Algorithms namely MultilayerPerceptron, Linear Regression, SMOReg, and Gaussian Processes, and compared their performances where the best-performed algorithm is used to develop a predictive model for rainfall accumulation. Results of the study found that Cagayan Valley accumulated 219.857 mm of rainfall and an average Tropical Cyclone Maximum Sustained winds of 123.438 kph. Tropical Cyclone Ulysses reached almost the highest value in all the attributes as evidence of its devastating nature last November 2020. Also, the Linear Regression algorithm revealed a better performance which displayed a higher correlation coefficient and lower error estimates and took less time in developing the model. Lastly, the results can be considered to be a basis of the impact of a Tropical Cyclone based on their attributes and can be implemented into technological platforms for predictive application which helps foresee the prediction rate of rain accumulation to develop techniques and preventive measures to target reduction of damage on properties and the number of casualties. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tropical cyclone

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Performance of Hybrid Stacking and Bagging Methods Based on machine learning Algorithms in the Classification of Dengue Fever Incidence Rate

Performance of Hybrid Stacking and Bagging Methods Based on ...

引用

Intelligent Cybernetics Technology & Applications (ICICyTA), international conference on

作者： Citakamalia Sri Suryani Prasetiyowati Yuliant Sibaroni Informatics School of Computing Telkom University Bandung Indonesia

Dengue Hemorrhagic Fever (DHF) is a disease that occurs every year. Bandung City is one of the regions in Indonesia with a high incidence rate of dengue fever cases which continues to increase from 2017 to 2021 at 49.66%. Therefore, this study aims to combine the machine learning algorithms, such as Naive Bayes, Support Vector machine, Decision Tree, and Random Forest with the Hybrid Stacking and Bagging method to improve accuracy in the number of DHF cases dataset from the 2017 to 2021 in Bandung City. The results show that the accuracy using Support Vector machine, Naive Bayes, Decision Tree, and Random Forest algorithms are 85.50%, 61.30%, 77.40%, and 80.60%, respectively. The accuracy increases after applying hybrid approach. The bagging method gets 88.70% accuracy, by using stacking method, the accuracy increased to 90.30%. The stacking method gets precision, recall, and f1-score of 90.62%, 90.94%, and 90.65%, respectively. From the results obtained it is evident that the hybrid approach by combining machine learning algorithms can improve classification accuracy results. The contribution of this research is to help the government in predicting and classifying the incidence rate of dengue fever to reduce the rate of cases in each region.

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Harmony: A Scheduling Framework Optimized for Multiple Distributed machine learning Jobs 41

Harmony: A Scheduling Framework Optimized for Multiple Distr...

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41st IEEE international conference on Distributed computing Systems (ICDCS)

作者： Lee, Woo-Yeon Lee, Yunseong Song, Won Wook Yang, Youngseok Kim, Joo Yeon Chun, Byung-Gon Samsung Res Seoul South Korea Seoul Natl Univ Seoul South Korea FriendliAI Seoul South Korea Qualcomm Technol Inc Seoul South Korea

ISBN: (纸本)9781665445139

We introduce Harmony, a new scheduling framework that executes multiple Parameter-Server ML training jobs together to improve cluster resource utilization. Harmony coordinates a fine-grained execution of co-located jobs with complementary resource usages to avoid contention and to efficiently share resources between the jobs. To resolve the memory pressure due to the increased number of simultaneous jobs, Harmony uses a data spill/reload mechanism optimized for multiple jobs with the iterative execution pattern. Our evaluation shows that Harmony improves cluster resource utilization by up to 1.65x, resulting in a reduction of the mean ML training job time by about 53%, and makespan, the total time to process all given jobs, by about 38%, compared to the traditional approaches that allocate dedicated resources to each job.

关键词： Training Measurement Adaptation models Schedules Processor scheduling conferences machine learning

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Activity Imputation of Shared e-Bikes Travels in Urban Areas 7th

Activity Imputation of Shared e-Bikes Travels in Urban Areas

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7th international conference on machine learning, Optimization, and Data Science (LOD) / 1st Symposium on Artificial Intelligence and Neuroscience (ACAIN)

作者： Hadjidimitriou, Natalia Selini Lippi, Marco Mamei, Marco Univ Modena & Reggio Emilia Dept Sci & Methods Engn Modena Italy

ISBN: (纸本)9783030954673;9783030954666

In 2017, about 900 thousands motorbikes were registered in Europe. These types of vehicles are often selected as the only alternative when the congestion in urban areas is high, thus consistently contributing to environmental emissions. This work proposes a data-driven approach to analyse trip purposes of shared electric bikes users in urban areas. Knowing how e-bikes are used in terms of trip duration and purpose is important to integrate them in the current transportation system. The data set consists of GPS traces collected during one year and three months representing 6,705 trips performed by 91 users of the e-bike sharing service located in three South European cities (Malaga, Rome and Bari). The proposed methodology consists of computing a set of features related to the temporal (time of the day, day of the week), meteorological (e.g. weather, season) and topological (the percentage of km traveled on roads with cycleways, speed on different types of roads, proximity of arrival to the nearest Point of Interest) characteristics of the trip. Based on the identified features, logistic regression and random forest classifiers are trained to predict the purpose of the trip. The random forest performs better with an average accuracy, over the 10 random splits of the train and test set, of 82%. The overall accuracy decreases to 67% when training and test sets are split at the level of users and not at the level of trips. Finally, the travel activities are predicted for the entire data set and the features are analysed to provide a description of the behaviour of shared e-bike users.

关键词： Trip imputation Travel activity behaviour e-bikes Activity detection GPS traces machine learning Random forest Multinomial logistic regression Safety

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Adequate Basis for the Data-Driven and machine-learning-Based Identification 1

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22nd international conference on Artificial Intelligence and soft computing, ICAISC 2023

作者： Rojahn, Marcel Ambros, Maximilian Biru, Tibebu Krallmann, Hermann Gronau, Norbert Grum, Marcus University of Potsdam Potsdam Germany Krallmann AG Berlin Germany

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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conference Paper Acceptance Prediction: Using machine learning 1

引用

2nd international conference on machine learning and Information Processing, ICMLIP 2020

作者： Joshi, Deepali J. Kulkarni, Ajinkya Pande, Riya Kulkarni, Ishwari Patil, Siddharth Saini, Nikhil Information Technology Department Vishwakarma Institute of Technology Bibvewadi Pune411037 India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

The paper presents a model that will predict the acceptance of the paper for a particular conference. The model is designed for the conferences, which accept researches done researches in the machine learning domain. The dataset that is used to develop this model is the ICLR 2017 (international conference Of learning Representation). The model gives its prediction based on extracted features. The features that most of the conferences consider are Number of References, Number of Figures, Number of Tables, Bag of words for ML related terms, etc. Some more features are taken into consideration to give better results such as Length of Title, Frequency of ML related words, Number of ML algorithms, Average length of sentences. The model is trained on the above-mentioned dataset, which contains 70 accepted and 100 rejected papers. For the prediction, different machine learning algorithms are used. The model is trained by applying algorithms such as Logistic Regression, Decision Tree, Random Forest, KNN, and SVM. The comparative study of different algorithms on the dataset gives the result that Decision Tress works effectively by providing 85% accuracy. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Frequency Domain Features Based Atrial Fibrillation Detection Using machine learning and Deep learning Approach 7

Frequency Domain Features Based Atrial Fibrillation Detectio...

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7th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2021

作者： Shrikanth Rao, S.K Kolekar, MaheshKumar H Martis, Roshan Joy VCET Department of ECE Puttur India

ISBN: (纸本)9781665428491

Atrial Fibrillation (AF) is a serious heart disease which can be diagnosed using Electrocardiogram (ECG). Early detection of AF is very important so that the morbidity and mortality can be reduced and the patient can have quality life. This paper proposes frequency domain analysis using power spectrum estimation using Welch, Auto Regression (AR) and Lomb scargle peridiogram methods. The single lead ECG recordings obtained from Physionet Challenge 2017 dataset are used for the analysis. The deep learning methods such as Temporal Convolution Network (TCN) and Deep Convolutional Neural Network (DCNN) are compared with traditional machine learning methods such as Decision Tree (DT) and Support Vector machine (SVM). The DCNN provided improved performance of 94.67% of accuracy which is superior compared to other methods. The proposed methodology can be used in practical hospital applications as adjunct/assisted tool for the physician. © 2021 IEEE.

关键词： Support vector machines

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