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On-Demand Triggered Memory Management Unit in Dynamic Binary Translator 15th

On-Demand Triggered Memory Management Unit in Dynamic Binary...

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15th International symposium on Advanced Parallel Processing Technologies, APPT 2023

作者： Xie, Benyi Li, Xinyu Yan, Yue Yan, Chenghao Liu, Tianyi Zhang, Tingting Yang, Chao Zhang, Fuxin SKLP Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China The University of Texas at San Antonio San Antonio United States Loongson Technology Co. Ltd Beijing China State Grid Liaoning Electric Power Supply Co. Ltd Shenyang China

ISBN: (纸本)9789819978717

User-level Dynamic Binary Translators (DBTs) linearly map the guest virtual memory to host virtual memory to achieve optimal performance. When the host page size exceeds the guest page size, multiple small guest pages are mapped to a single large host page, resulting in inappropriate permissions mapping. DBTs face security and correctness risks accessing the inappropriately mapped host page. Our survey reveals that most of the state-of-the-art user-level DBTs suffer from these risks. While system-level DBT can avoid these risks through a software Memory Management Unit (MMU). However, the software MMU fully emulates guest memory management, leading to slower performance than the linear mapping approach of user-level DBTs. To address the balance of performance and risks, we propose a DBT memory management method named On-Demand Triggered MMU (ODT-MMU), that combines the strengths of both user-level and systemlevel DBTs. ODT-MMU utilizes linear mapping for non-risky page accesses and triggers a software MMU when accessing risky pages. We implement ODT-MMU in two ways to accommodate various application scenarios: a platform-independent implementation named ODTInterpMMU, and a hardware-accelerated implementation named ODTManipTLB. ODT-ManipTLB is designed for host Instruction Set architectures (ISAs) that support programmable TLB. Experimental results demonstrate that both implementations can effectively mitigate risks associated with page size. Furthermore, ODT-ManipTLB achieves over 2000x performance improvement compared with the ODT-InterpMMU, while maintaining comparable performance to the DBT without ODTMMU. Additionally, our work is applied to two industrial DBTs, XQM and LATX. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Mapping

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SwiftFrame: Developing Low-latency Near Real-time Response Framework

SwiftFrame: Developing Low-latency Near Real-time Response F...

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International Conference on computing and Networking Technology (ICCNT)

作者： Swargam Saipranith Anand Kumar Singh Neha Agrawal Sruthi Chilumula Computer Science and Engineering IIIT Sri City Chittoor AP India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In today’s era, handling of big data poses significant challenges due to its massive volume and diverse formats. A rapid processing of such diverse data formats, spanning vast volumes, is essential for delivering swift and effective real-time responses. this paper proposes a near real-time response framework named as SwiftFrame for low-latency, high-speed applications. Leveraging Docker, the proposed architecture seamlessly deploys and manages Apache Kafka, Apache Zookeeper, and PostgreSQL. Apache Flink efficiently processes data streams from Kafka, orchestrates tasks, and stores results in PostgreSQL. Grafana enables real-time visualization, offering a comprehensive monitoring interface. the proposed architecture, SwiftFrame, is designed to handle modern data processing requirements, ensures low latency and high throughput, suitable for diverse high-speed applications. In the experimental setup, four different scenarios have been considered and each scenario has repeated 5 times. the experimental results show that the proposed architecture outperforms in terms of latency and throughput achieving an average latency of 0.13 sec and an average throughput of 8.7 records per second. the future research directions of this paper are also highlighted.

关键词： Data visualization computer architecture Big Data throughput Data processing Real-time systems Low latency communication Streams Monitoring

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Efficient Trajectory Prediction for Autonomous Vehicles: A Region-Based Subsampling Approach

Efficient Trajectory Prediction for Autonomous Vehicles: A R...

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Siddharth Raina Carnegie Mellon University USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Trajectory prediction is a vital component of autonomous driving systems, enabling vehicles to antici-pate the future movements of surrounding agents and ensure safe navigation. Recent advancements in transformer-based architectures have significantly improved the accuracy of trajectory prediction by capturing complex interactions and dependencies among agents and map features. However, their computational overhead, stemming from processing high-dimensional map data, poses challenges for real-time applications. In this paper, we propose a novel Region-Based Subsampling with Priority approach that reduces the computational cost of transformer-based trajectory prediction models by retaining only the most relevant map features. Our method divides the map into regions, assigns priority scores based on proximity and criticality, and aggregates tokens within the highest-priority regions. this strategy drastically reduces the number of tokens, achieving up to 91% reduction in self-attention complexity, while maintaining prediction accuracy. We evaluate our approach on the INTERACTION dataset using a Wayformer-based architecture, demonstrating comparable performance to the baseline in key metrics such as ADE, FDE, minADE, and minFDE. Additionally, qualitative analysis highlights improved trajectory reasoning due to enhanced map feature selection. the proposed method's efficiency and simplicity make it suitable for real-time deployment in computationally constrained autonomous systems.

关键词： Measurement Accuracy Navigation computer architecture Predictive models Transformers Real-time systems Trajectory Computational efficiency Autonomous vehicles

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Classification of Histology Images Based on a Compact 3D Representation 1

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15th International Conference on Pattern Recognition and Information Processing, PRIP 2021

作者： Brancati, Nadia Cicala, Crispino Frucci, Maria Riccio, Daniel Institute for High Performance Computing and Networking National Research Council of Italy Naples Italy Computer Scientist Milan Italy University "Federico II" Naples Italy

ISBN: (数字)9783030988838

ISBN: (纸本)9783030988821

Although the Convolutional Neural Networks (CNNs) have been widely adopted for the classification of histopathology images, one of the main drawbacks of CNNs is their inability to cope with gigapixel images. To deal with the high resolution of Whole Slide Image (WSI), many methods focus on patch processing which can result in improper representation if the patches are analyzed independently, losing the context information that is fundamental in digital pathology. In this study, the WSI is mapped into a compressed representation preserving the topological and morphological information relating to spatial correlations of neighboring patch features of the WSI. Such a representation is used to train a CNN to solve a classification task of breast histological images. the effectiveness of the suggested framework is demonstrated through experiments on Camelyon16 dataset. the results show the performance of our approach when three different ways to incorporate the spatial correlation in the tensor are used singly or in combination, highlighting that it is comparable with the state of the art. © 2022, Springer Nature Switzerland AG.

关键词： Image classification

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Hydis: A Hybrid Consistent KVS with Effective Sync Among Replicas 15th

Hydis: A Hybrid Consistent KVS with Effective Sync Among Rep...

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15th International symposium on Advanced Parallel Processing Technologies, APPT 2023

作者： Lou, Junsheng Xu, Zichen School of Mathematics and Computer Sciences Nanchang University Nanchang China

ISBN: (纸本)9789819978717

Distributed storage systems distribute user loads across regions. User requests from different geographical locations are directed to the nearest data center, benefiting reduced service latency and improved service quality. However, the consistency among regions holds against availability and richness of the underlying data services. To address these constraints, our study proposes Hydis, a hybrid consistency distributed key-value storage system based on optimized replica synchronization. Hydis guarantees high availability and scalability for geographically distributed systems and uses Conflict-free Replicated Data Types to construct HybridLattice that supports various consistency models. A novel Writeless-Consistency strategy is proposed to improve the synchronization efficiency between replicas, and a dynamic synchronization optimization based on this strategy is implemented for consistency algorithm to effectively reduce the synchronization overhead of distributed storage systems. A performance evaluation of the Hydis cluster deployed on Alibaba Cloud showed that the strong consistency algorithm in Hydis outperformed the Raft algorithm by 1.8X. Additionally, the causal consistency algorithm in Hydis outperformed the traditional Vector Clock algorithm by 2.5X. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Synchronization

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TangramFP: Energy-Efficient, Bit-Parallel, Multiply-Accumulate for Deep Neural Networks

TangramFP: Energy-Efficient, Bit-Parallel, Multiply-Accumula...

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International symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Yuan Yao Xiaoyue Chen Hannah Atmer Stefanos Kaxiras Uppsala University Sweden

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

As energy consumption becomes a primary concern for deep learning acceleration, the need to optimize not only data movement but also compute is becoming important. the basic element of compute, the Multiply-Accumulate (MAC) unit, performs the operation X · Y+Z, comprises the compute cores of systolic arrays such as Google’s TPU or Nvidia’s Tensor Cores, and it is found in practically every deep neural network (DNN) *** this work, we aim to reduce the energy needs of bit-parallel MACs, without perceptible impact on precision, and without affecting the structure of the overall accelerator architecture—in other words, we aim for an energy-efficient drop-in MAC *** there is a significant body of work on efficient approximate multipliers and MACs, in this work, we propose a novel approach: a tunable floating-point MAC design, TANGRAMFP, that can deliver the full precision of a standard implementation, yet dynamically adjusts to eliminate ineffectual computation. Different from state-of-the-art approaches that are based on truncated multiplication, TANGRAMFP introduces a new class of multipliers where input operands are split and partial products are selectively generated (by enabling or disabling different areas of the logical multiplier array) and added together. In a hardware implementation, this is achieved by decomposing a large multiplier into four smaller ones, at the same overall hardware *** demonstrate that TANGRAMFP precision can adhere to the same bounds (measured as Unit-in-Last-Place—ULP—error) as standard IEEE FP16 arithmetic and delivers better precision than a state-of-the-art approach based on bit-serial truncated multiplication aimed at eliminating ineffectual computation in DNNs. At the same time, TANGRAMFP is a drop-in replacement for the standard MAC design, having approximately the same mean ULP error, the same area and latency, while achieving up to 36.57% dynamic power savings (27.44% with a mean error close to stan

关键词： Energy consumption Tensors Measurement units high performance computing Artificial neural networks Hardware Energy efficiency Systolic arrays Standards Logic arrays

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Efficiency of VANET Routing Algorithms in Traffic Use Cases 15

Efficiency of VANET Routing Algorithms in Traffic Use Cases

引用

15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2023

作者： Vanek, Michal Michalek, Libor Lauterbach, Filip VSB - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science 17. listopadu 2172/15 Ostrava708 00 Czech Republic

ISBN: (纸本)9798350393286

Vehicular Ad-Hoc Networks (VANET) can assist in reducing traffic jams, travel time, and CO2emissions. they can also improve safety, increase the number of cars travelling through intersections, enable effective prioritization of selected vehicles, and potentially provide resources for cloud computing. the paper presents the results of simulations from the SUMO and NS3 simulation tools applied to VANET network use cases. Simulations were performed for two selected intersections in the city of Ostrava at peak traffic. the performance of different routing protocols (AODV, DSDV, OLSR) was assessed according to several suitable indicators. Finally, the paper discusses the use of simulation tools in determining a robust and effective routing protocol for all possible applications of VANET. © 2023 IEEE.

关键词： AODV communication DSDV intersection junction NS-3 OLSR SUMO traffic VANET vehicle

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Neural Machine Translation Using a Pivot Approach: Dogri to English

Neural Machine Translation Using a Pivot Approach: Dogri to ...

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International Conference on computing and Networking Technology (ICCNT)

作者： Aarathi Rajagopalan Nair Shailashree K. Sheshadri A. Dhanush N.V.S Pradyumna Deepa Gupta Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the progress of Neural Machine Translation for Indic languages is gradually achieving a noteworthy result in terms of translation accuracy. However, its advancement is impeded by the lack of rich parallel corpora, a limitation not shared by non-Indic languages. As a result, most languages of India are still categorized as low-resource or zero-resource languages. Various state-of-the-art architectures have been developed to address these challenges, with the predominant approach being the pivot-based technique. this technique, rooted in transfer learning, utilizes an auxiliary language as a pivot to assist in translation. In this study, a pivotbased Neural Machine Translation (NMT) architecture is designed for translating Dogri to English, with Hindi as the pivot. Dogri, being a low-resource language, shares linguistic similarities with Hindi. Furthermore, Hindi-to-English state-of-the-art models are accessible, ensuring high translation accuracy. through the implementation of this pivot-based translation model, a BLEU score of 20.97, SacreBLEU of 21.54 and chrF of 50.01 is attained.

关键词： Accuracy Computational modeling Transfer learning Neural networks Neural machine translation computer architecture Linguistics

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FlowADGAN: Adversarial Learning for Deep Anomaly Network Intrusion Detection 18th

FlowADGAN: Adversarial Learning for Deep Anomaly Network In...

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18th International Workshop on Security and Trust Management, STM 2022, co-located with the 27th European symposium on Research in computer Security, ESORICS 2022

作者： Wang, Pan Li, Zeyi Zhou, Xiaokang Su, Chunhua Wang, Weizheng School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Faculty of Data Science Shiga University Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Division of Computer Science University of Aizu Fukushima965–8580 Japan Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9783031295034

Due to the increasingly evolved attacks on the Internet, especially IoT, 5G, and vehicle networking, a robust Network Intrusion Detection System (NIDS) has gained increasing attention from academic and industrial communities. Anomaly-based intrusion detection algorithms aim to detect unexpected deviations in the expected network behaviour, thus detecting unknown or novel attacks compared to signature-based methods. Deep Anomaly Detection (DAD) technologies have attracted much attention for their ability to detect unknown attacks without manually building the traffic behaviours profile. However, low recall rates and high dependencies on data labels still hinder the development of DAD technologies. Inspired by the successes of Generative Adversarial Networks (GANs) for detecting anomalies in the area of computer Vision and Images, we have proposed a deep end-to-end architecture called FlowADGAN for detecting anomalies in NIDS. Unlike traditional GAN-based NIDS methods that usually construct Generator (G) and Discriminator (D) based on vanilla GAN, the proposed architecture is composed of a flow encoder-decoder-encoder for G, and a flow encoder for D. FlowADGAN can learn a latent flow feature space of G so that the latent space better captures the normality underlying the network traffic data. We conduct several experimental comparisons with existing machine learning algorithms like One-Class SVM, LOF, and PCA and existing deep learning methods, including AutoEncoder and VAE, on three public datasets, NSL-KDD CICIDS2017 and UNSW-NB15. the evaluation results show that FlowADGAN can significantly improve the performance of the anomaly-based NIDS. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Node Confidence Determination Based Moving Target Defense Strategy for SDN 15

A Node Confidence Determination Based Moving Target Defense ...

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15th International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Sun, Shimin Xu, Guowei Han, Li Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong Tianjin China Tianjin Univ. of Technol. Natl. Eng. Laboratory for Computer Virus Prevention and Control Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350308693

With the rapid development of Internet technology, network assets have become an indispensable part of modern society. Especially in the face of high-value network assets, the attacker is faced with paying a greater cost to study the network structure. Due to the unknown attacker and attack means, the attacker has an advantage in the attack and defense situation. the moving target defense technique can flexibly change the static characteristics of the network attack surface and increase the difficulty for the attacker to detect the network, thus achieving the purpose of enhancing network security. Based on this technique, this paper proposes a defense strategy based on node confidence. It puts forward stricter constraints on the differential selection of address/port hopping paths, dynamically selects the hopping objects according to the node's confidence, and implements the defense strategy of differential complexity. this further increases the network's uncertainty and the difficulty for the attacker to detect the network structure. the experimental results show that in the path differentiation comparison, the maximum improvement is about 18% compared to the Dynamic Random Route Hopping strategy. In the CPU load experiment, the CPU load rate is reduced by about 4.625% on average compared to the Double Address Hopping strategy. Moreover, for larger hopping period, the proposed strategy also performs well in terms of CPU load rate. the proposed strategy also exhibits good performance in terms of CPU load ratio for large hopping cycles. © 2023 IEEE.

关键词： Network security

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