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Analyzing Memory Access Traces of Deep Learning Workloads for Efficient Memory Management 12

Analyzing Memory Access Traces of Deep Learning Workloads fo...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Lee, Jeongha Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350310153

Due to the recent advances in artificial intelligence (AI) and high-performance computing technologies, deep learning is actively used in various application domains of the fourth industrial revolution. Since the data size of deep learning increasingly grows, analyzing the memory reference characteristics of AI workloads becomes important. In this article, we analyze the memory access traces of deep learning workloads consisting of text and image data. Specifically, we analyze read and write operations separately for instruction and data memory accesses. Based on our analysis, we find out the following characteristics that are quite different to memory access traces of conventional desktop workloads. First, when comparing instruction and data accesses, instruction access accounts for a little portion of memory accesses in deep learning workloads, which is quite different from traditional workloads. Specifically, instruction access accounts for 1-3.3% in deep learning workloads while 15-30% in traditional workloads. Second, when comparing read and write accesses, write access accounts for 64-80%, which is also different to traditional workloads where write accounts for 6-55%. third, although write access accounts for the majority of memory accesses, it exhibits the low access skewness. Specifically, the Zipf parameter of write access is about 0.3, and the parameter is smaller in text workloads than image workloads. Fourth, in predicting re-reference likelihood, recency ranking is important in instruction and data read accesses, but frequency ranking is necessary in write accesses for accurate estimation. We expect that the characterization study in this article will guide for managing memory systems of deep learning workloads. © 2022 IEEE.

关键词： Memory architecture

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A Two-Stage Graph Computation Model with Communication Equilibrium 15th

A Two-Stage Graph Computation Model with Communication Equil...

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15th CCF Conference on computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Dong, Yanmei Chen, Rongwang Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China College of Mathematics and Computer Science Wuyi University Wuyishan Fujian354300 China Digital Fujian Tourism Big Data Institute Wuyishan Fujian354300 China

ISBN: (纸本)9789811625398

Distributed graph computing aims at performing in-depth analysis on large networks in a parallel manner. Iterative communication computation is an important model to perform graph analysis. Moreover, high-efficiency iterative communication computation is necessary for assuring the quality of graph partitioning. Many strategies to improve graph computing are usually based on the hypothesis of single communication iteration which focuses on the optimization of load balance in a single graph partitioning and the improvement of parallel granularity or communication manners. However, a graph in the real-world usually requires complex communication iterations to achieve good analysis results, which often have the problems of data and communication tilting and low analysis efficiency. In this paper, we propose a two-stage graph computing model with communication equilibrium (TSMCE). the model employs a communication-equilibrated graph partitioning strategy (CEGP) for load balancing and a two-stage graph computing mode (TS) to reduce the crossing-partition communication. With the change of graph density, various factors such as load balancing, communication balancing, cross-partition transmission traffic, communication delay, and convergence speed can always maintain an efficient balance. the experiments conducted on the real-world datasets show that the communication-equilibrated graph partitioning strategy can divide a graph with high quality compared with Hash and Metis. Besides, the overall performance of our two-stage graph computing model with communication equilibrium is higher than that of the BSP model. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Investigating Cross-Lingual Transfer Learning Techniques for Urdu Text Using Word Embeddings 15

Investigating Cross-Lingual Transfer Learning Techniques for...

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15th International Conference on Open Source Systems and Technologies, ICOSST 2021

作者： Rehman, Shujah Ur Tahir, Bilal Mehmood, Muhammad Amir Al-Khawarizmi Institute of Computer Science UET Lahore Pakistan

ISBN: (纸本)9781665413053

the plethora of online content has paved the way for the development of sophisticated and advanced Natural Language Processing (NLP) and Information Retrieval (IR) tools. However, such tools are only available for English and other high-resource languages while being unavailable for low-resource languages such as Urdu. In this regard, generally, cross-lingual transfer learning techniques are adopted to utilize tools developed for the English language for low resource languages. In this paper, we evaluate the performance of three word-level transfer learning methods: OrthoMap, VecMap-supervised, and VecMap unsupervised for Urdu text. We further test these transfer learning methods for three tasks: propaganda identification, topic classification, and sentiment analysis. For this purpose, we augment an English-Urdu word dictionary and three datasets of Ur-En Propaganda, Ur-En News Dataset, and Ur-En Sentiment Corpus. Our analysis shows that the transfer learning methods optimize better for the short-text of Ur-En Sentiment Corpus with a precision of 40.1%. While for propaganda detection, the classifier attained an accuracy of 83% after transfer learning which is competitive with the 87% accuracy achieved after training the model on Urdu text data. We believe that this work will be beneficial for NLP, IR, and computational linguistic researchers working on Urdu language content. © 2021 IEEE.

关键词： Sentiment analysis

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Sentient Spaces: Intelligent Totem Use Case in the ECSEL FRACTAL Project

Sentient Spaces: Intelligent Totem Use Case in the ECSEL FRA...

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Euromicro symposium on Digital System Design

作者： Federica Caruso Tania Di Mascio Daniele Frigioni Luigi Pomante Giacomo Valente Stefano Delucchi Paolo Burgio Manuel Di Frangia Luca Paganin Chiara Garibotto Damiano Vallocchia Università degli Studi dell' Aquila AITEK Srl Università degli Studi di Modena e Reggio Emilia MODIS Srl Rulex Srl Università degli Studi di Genova Ro Technology Srl

ISBN: (数字)9781665474047

ISBN: (纸本)9781665474054

the objective of the FRACTAL project is to create a novel approach to reliable edge computing. the FRACTAL computing node will be the building block of scalable Internet of things (from Low computing to high computing Edge Nodes). the node will also have the capability of learning how to improve its performance against the uncertainty of the environment. In such a context, this paper presents in detail one of the key use cases: an Internet-of-things solution, represented by intelligent totems for advertisement and wayfinding services, within advanced ICT-based shopping malls conceived as a sentient space. the paper outlines the reference scenario and provides an overview of the architecture and the functionality of the demonstrator, as well as a roadmap for its development and evaluation.

关键词： Wireless communication Uncertainty Digital systems architecture computer architecture Fractals Reliability

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Advanced Skin Disease Classification with VGG19, YOLOv3, and MobileNet

Advanced Skin Disease Classification with VGG19, YOLOv3, and...

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International Conference on computing and Networking Technology (ICCNT)

作者： Himani Bhatia Satvik Vats Rishabh Sharma Lovely Professional University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Skin diseases are classified into various types hence proper classification of the disease is very important because its determination of the type of treatment to offer the patients will highly determine the results of the patients. this research paper presents an advanced approach to skin disease classification by employing three state-of-the-art deep learning models: VGG19 – a convolutional model that is 19 layers deep, YOLOv3 – an improved version of the YOLO algorithm, MobileNet – a lightweight neural network designed for mobile devices. the performance of these models will be assessed and compared to identify the most effective way of categorizing numerous skin diseases using a large data set. ResNet and VGG19 are employed based on their resilience in feature extraction, and despite the model’s complexity, it is rather accurate. When it comes to the detection of skin abnormalities, object localization is critical, for which YOLOv3 is used because of its high accuracy. MobileNet is evaluated for its applicability in real-time classification for mobile and integrated embedded device applications. the dataset is composed of skin lesion images and has high resolutions and dermatoscopic images, which means that the models had a diverse set of skin conditions on which to train. Metrics including accuracy, precision, recall, and F1-score are used to assess and rank the efficiency of one specific model with another. In highlighting the strengths and weaknesses of each model, the findings help in informing studies applying such frameworks in real-world practice contexts. this present comparative study intends to Provide an Improved understanding in the field of dermatology With Better Machine Learning Algorithms to Automate skin Disease Identification, with an impressive patient care impact.

关键词： YOLO Deep learning Accuracy Machine learning algorithms Computational modeling Skin Real-time systems Lesions Diseases Load modeling

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Technology-Assisted computing-In-Memory Design for Matrix Multiplication Workloads 15

Technology-Assisted Computing-In-Memory Design for Matrix Mu...

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15th IEEE/ACM International symposium on Nanoscale architectures, NANOARCH 2019

作者： Jao, Nicholas Srivinasa, Srivatsa Ramanathan, Akshay Kim, Minhwan Sampson, John Narayanan, Vijaykrishnan Pennsylvania State University School of Electrical Engineering and Computer Science Pennsylvania United States

ISBN: (数字)9781728155203

ISBN: (纸本)9781728155203

Recent advances in emerging technologies such as monolithic 3D Integration (M3D-IC) and emerging non-volatile memory (eNVM) have enabled to embed logic operations in memory. this alleviates the "memory wall" challenges stemming from the time and power expended on migrating data in conventional Von Neumann computing paradigms. We propose a M3D SRAM dot-product engine for compute in-SRAM support used in applications such as matrix multiplication and artificial neural networks. In addition, we propose a novel computing in RRAM-based memory architecture to efficiently solve the computation intensity of sparse dot products. Specifically, the index assessment of sparse matrix-vector multiplication used in support vector machines (SVM). At maximum throughput, our proposed RRAM architecture achieves 11.3× speed up when compared against a near-memory accelerator. © 2019 IEEE.

关键词： Static random access storage

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GPT-4 Meets TF-IDF: A Hybrid Approach for Detecting Spam Emails Using Machine Learning

GPT-4 Meets TF-IDF: A Hybrid Approach for Detecting Spam Ema...

引用

IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Sherif E. Abdelhamid Benjamin Davis Dang Khoa Le Computer & Information Sciences Virginia Military Institute Lexington VA USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Spam emails, designed to flood email inboxes with unwanted, irrelevant, or harmful content, have evolved into a persistent cybersecurity threat. Modern spam emails span multiple categories, including advertising spam, which promotes products or services; scam spam, which attempts to deceive recipients into revealing sensitive information; and malicious spam, which carries phishing links or malware. Traditional techniques usually detect spam emails by analyzing specific word patterns or applying a rule-based filtering system. However, spammers now employ more sophisticated language to evade detection, making it harder to recognize spam emails. Traditional methods may overlook subtle contextual and behavioral indicators critical for distinguishing spam from legitimate emails. Recent advancements in large language models present new opportunities by capturing complex patterns related to urgency, requests for sensitive information, and deceptive linguistic structures. this study explores a hybrid approach that combines TF-IDF (Term Frequency-Inverse Document Frequency) with GPT-4 (Generative Pre-trained Transformer) derived features to enhance spam detection, especially with classifiers that benefit from diverse feature sets. We evaluate this combined feature set across multiple machine learning classifiers, including K-Nearest Neighbors, Decision Tree, Naive Bayes, and Random Forest. Our findings reveal that this hybrid approach significantly improves model performance, with Random Forest achieving high accuracy (99.86%), precision (98.46%), and recall (100%). Notably, other classifiers, like K-Nearest Neighbors and Decision Tree, benefit from the added contextual and behavioral cues. the results demonstrate the importance of a multifaceted approach for detecting spam emails, where linguistic patterns and contextual features together can capture the deceptive tactics used by spammers. this research demonstrates the value of combining term-based methods with advanced la

关键词： Unsolicited e-mail Computational modeling Phishing Nearest neighbor methods Linguistics Feature extraction Transformers Frequency diversity Decision trees Random forests

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Resource Efficient Hardware Implementation of Multi-Variate Regression Training

Resource Efficient Hardware Implementation of Multi-Variate ...

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Applied computing (URC), Annual Undergraduate Research Conference on

作者： Ziyad Alshamrani Mamdouh Almatrafi Ahmad Aldkhil Computer Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331527341

ISBN: (纸本)9798331527358

Multi-variate linear regression is one of the most popular supervised machine learning algorithms that is used to find a linear relation between independent variables and a single dependent variable. It has been successfully utilized in predicting dependent variables in many applications. Due to the wide adoption of machine learning algorithms, researchers have been developing ever more efficient implementations of such algorithms. Hardware implementations can offer high computational power and parallel processing. Field Programmable Gate Arrays (FPGAs) offer a very attractive hardware implementation option. FPGAs offer a flexible architecture that allows for algorithm modifications as needed. they can outperform GPUs and CPUs in terms of speed and power efficiency. In this paper, we propose a resource-efficient hardware implementation of a multi-variate linear regression training circuit on FPGA using Verilog HDL. this design leverages the available Block RAMs (BRAMs) on the FPGA board to store the data samples before feeding them to the algorithm. It is parametrizable in terms of both the number of data samples and the number of features in the data. However, a re-synthesis of the design is required when the number of features changes. We tested the design using a 200-training data set with 10 features and the time needed to produce the coefficients was 2.101 milliseconds. We synthesized the design using a low-cost FPGA board with a Xilinx xc7a100t-3-fgg484 FPGA device. the design only utilized 4% of the available registers, 8% of the available Logic Units, 5% of the available block RAMs, and 10% of the available DSP48E1 blocks. the estimated average power used was 130.84 milliwatts. A Python implementation of the same algorithm took 11.5921 milliseconds to compute the results using an Intel Core i7-10510U Processor with an average power of 15 W. Hence, the hardware implementation achieved a 5.5x reduction in computation time and a 115x reduction in power consumpt

关键词： Training Machine learning algorithms Power demand Linear regression Random access memory Training data Hardware

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Numerical simulation of segmented ratio in bismuth telluride and skutterudites for waste heat recovery 15

Numerical simulation of segmented ratio in bismuth telluride...

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15th International Engineering and computing Research Conference, EURECA 2021

作者： Cheong, K.W. Lim, J.H. School of Computer Science and Engineering Faculty of Innovation and Technology Taylor's University Lakeside Campus No. 1 Jalan Taylor's Selangor Subang Jaya47500 Malaysia

the thermoelectric performance of the segmented annular thermoelectric generators with the bismuth telluride and skutterudites has been investigated. the effect of the length ratio of the hot-segment leg to total length leg on the thermoelectric performance of the segmented annular thermoelectric generators is analysed and discussed and the optimization design of the annular thermoelectric generator with bismuth telluride and skutterudites as the materials with high thermoelectric performance is obtained. the result of the thermoelectric performance with the manipulated variable of the increase of length ratio, the output power, output voltage and efficiency of the segmented annular thermoelectric generators increase at the beginning then decrease afterwards. Additionally, to compare with the single bismuth telluride and skutterudites annular thermoelectric generators, the output voltage, output power and the conversion efficiency of the segmented annular thermoelectric generators can be improved twice. Lastly, the thermoelectric performance of the segmented annular thermoelectric generators operating in the changes of the temperature. the result has proved that as the temperature increase, the thermoelectric performance of the annular thermoelectric generator will also increase. Hence, the acquired results may be given some useful applications of the bismuth telluride and skutterudites on the segmented annular thermoelectric generators for waste heat recovery. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Skutterudites

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A Dataflow architecture for Real-Time Full-Search Block Motion Estimation 17th

A Dataflow Architecture for Real-Time Full-Search Block Moti...

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17th International symposium on Applied Reconfigurable computing, ARC 2021

作者： Barba, Jesús Caba, Julián Escolar, Soledad Torre, Jose A. De La Rincón, Fernando López, Juan C. School of Computer Science University of Castilla-La Mancha Ciudad Real13071 Spain

ISBN: (纸本)9783030790240

Motion estimation is the cornerstone of main video compression standards, which are based on the reduction of the temporal redundancy between consecutive frames. Although the mechanism is simple, the best method, Full Search, uses a brute-force approach which is not suited for real-time applications. this work introduces a high performance architecture for performing on-the-fly full-search block matching estimation in FPGA devices, which has been modeled using C++ programming language and synthesized with Vivado HLS for a Xilinx ZC706 prototyping board. the architecture is based on a dataflow datapath and it is configurable, enabling a fast and easy exploration of the solution space. On-board results achieve a maximum performance of 743 fps, 247 fps and 110 fps for VGA, HD and FHD video resolutions, respectively, for a typical macroblock size of 16 × 16 pixels and a search area of ± 16 pixels. © 2021, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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