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检索条件"任意字段=International Conference on Embedded Computer Systems -Architectures, Modeling and Simulation"

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EDRA: A Hardware-Assisted Decoupled Access/Execute Framework on the Digital Market 21st

EDRA: A Hardware-Assisted Decoupled Access/Execute Framework...

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21st international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Theodoropoulos, Dimitris Brokalakis, Andreas Alachiotis, Nikolaos Pnevmatikatos, Dionisios Tech Univ Crete Telecommun Syst Inst Campus Akrotiri Khania 73100 Greece Univ Twente Comp Architecture Embedded Syst Fac EEMCS Enschede Netherlands Natl Tech Univ Athens Inst Commun & Comp Syst Athens Greece

ISBN: (纸本)9783031045806;9783031045790

EDRA was an Horizon 2020 FET Launchpad project that focused on the commercialization of the Decoupled Access Execution Reconfigurable (DAER) framework - developed within the FET-HPC EXTRA project - on Amazon's Elastic Cloud (EC2) Compute FPGAbased infrastructure. The delivered framework encapsulates DAER into a EC2 virtual machine (VM), and uses a simple, directive-based, highlevel application programming interface (API) to facilitate application mapping to the underlying hardware architecture. EDRA's Minimum Viable Product (MVP) is an accelerator for the Phylogenetic Likelihood Function (PLF), one of the cornerstone functions in most phylogenetic inference tools, achieving up to 8x performance improvement compared to optimized software implementations. Towards entering the market, research revealed that Europe is an extremely promising geographic region for focusing the project efforts on dissemination, MVP promotion and advertisement (EDRA was funded by the European Union's Horizon 2020 research and innovation programme "FET Innovation Launchpad" under grant agreement No 851631).

关键词： Cloud computing FPGAs Decoupled Access-Execute

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Energy-Efficient and High-Throughput CNN Inference on embedded CPUs-GPUs MPSoCs 21st

Energy-Efficient and High-Throughput CNN Inference on Embedd...

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21st international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Tang, Erqian Minakova, Svetlana Stefanov, Todor Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9783031045806;9783031045790

Nowadays, many application scenarios, such as mobile phones, drones, mobile robots, require Convolutional Neural Networks (CNNs) inference on embedded CPUs-GPUs MPSoCs. CNN model inference is usually computation intensive while the embedded CPUs-GPUs MPSoCs are usually energy consumption constrained. Therefore, how to achieve computationally-intensive CNN inference in an energy-efficient and high-throughput way is an important issue. However, existing Deep Learning (DL) frameworks only pay attention to achieving high-throughput inference when deploying CNN models on CPU or GPU processors without specifically considering the energy consumption. In this paper, we propose a novel methodology which features design-time optimization techniques in order to achieve energy efficiency and high throughput when deploying CNN models on embedded CPUs-GPUs MPSoCs. Our methodology finds Pareto-optimal mappings of a CNN model onto a CPUs-GPUs MPSoC with voltage and frequency scaling (VFS) configurations with the help of a two-objective Genetic Algorithm (GA) which optimizes the system throughput and energy consumption simultaneously. Moreover, we propose two analytical models, that are used as fitness functions in the two-objective GA to evaluate very fast the system throughput and energy consumption of CNNs mapped onto embedded CPUs-GPUs MPSoCs. Also, we confirm the high accuracy of these two analytical models by experimental evidence. Finally, our experimental results show that our novel methodology is able to achieve both energy efficiency and high throughput when deploying CNN models on embedded CPUs-GPUs MPSoCs, in comparison with TensorRT which is the best-known CNN deployment optimizer designed for NVIDIA embedded MPSoCs.

关键词： Convolutional Neural Networks SDF Pareto-optimal mapping High-throughput Energy-efficient MPSoCs TensorRT

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VULDAT: Automated Vulnerability Detection from Cyberattack Text 23rd

VULDAT: Automated Vulnerability Detection from Cyberattack T...

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23rd international conference on embedded computer systems: architectures, modeling, and simulation, SAMOS 2023

作者： Othman, Refat Russo, Barbara Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy Free University of Bozen-Bolzano Bolzano Italy

ISBN: (纸本)9783031460760

The existing literature on the connection between attacks and system vulnerabilities often relies on manual techniques. We have developed an approach, called VULDAT, to automatically identify software vulnerabilities and weaknesses from the text of an attack by leveraging the information contained in MITRE repositories and datasets that contain descriptions of attacks and information about attack methods, as well as code snippets that describe the related weaknesses of vulnerabilities. Thus, this research focuses on analyzing attack text descriptions and predicting their effects using natural language processing (NLP) and machine learning techniques. This can be helpful to quickly examine new attacks found in the real world and assist security experts in taking the appropriate actions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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Autism Disease Prediction Using Deep Learning & Transfer Learning 2

Autism Disease Prediction Using Deep Learning & Transfer Lea...

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2nd international conference on Networking, embedded and Wireless systems, ICNEWS 2024

作者： Das, Abhishek Alam, Towhidul Hossain, Md. Zihad Lamiya, Habiba Rashid North South University Department of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

The neurological disease known as autism spectrum disorder (ASD) is characterized by impaired social interaction, communication issues, and constrained and repetitive behavior patterns. For the benefit of early interventions and support for afflicted persons, timely and accurate ASD prognosis is essential. Deep learning methods have become effective tools for predictive modeling across a range of industries, including healthcare. This study examines the use of deep learning and transfer learning to forecast ASD using a large dataset of clinical and behavioral variables. In this study, the effectiveness of three well-known deep learning architectures VGG16, DenseNet121, and MobileNetv2 in predicting ASDs is compared. A sizable dataset with a variety of ASD-related variables, such as demographic data, medical histories, and behavioral assessments, is used to train the models. To take use of pre-learned weights from models trained on extensive generic image recognition tasks, transfer learning is used. With accuracy rates of 97% apiece, the experimental results show remarkable prediction performance for VGG16 and DenseNet121. These models have significant generalization abilities that make it possible to make reliable predictions for identifying those who are at risk for ASD. In contrast to the other architectures, MobileNetv2 only obtains an accuracy of 73%. The results show that deeper architectures like VGG16 and DenseNet121 capture the rich patterns and fine details of the input data, resulting in more precise predictions. Additionally, thorough investigations are carried out to look into the models' learned representations and pinpoint the primary features that influence ASD prediction. These revelations aid in a better comprehension of the underlying causes and potential biomarkers of ASD. The information gleaned from these studies can direct ongoing research projects and support the creation of individualized interventions and therapies. Overall, the study empha

关键词： Adversarial machine learning

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parti-gem5: gem5’s Timing Mode Parallelised 23rd

parti-gem5: gem5’s Timing Mode Parallelised

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23rd international conference on embedded computer systems: architectures, modeling, and simulation, SAMOS 2023

作者： Cubero-Cascante, José Zurstraßen, Niko Nöller, Jörn Leupers, Rainer Joseph, Jan Moritz Institute for Communication Technologies and Embedded Systems RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031460760

Detailed timing models are indispensable tools for the design space exploration of Multiprocessor systems on Chip (MPSoCs). As core counts continue to increase, the complexity in memory hierarchies and interconnect topologies is also growing, making accurate predictions of design decisions more challenging than ever. In this context, the open-source Full System Simulator (FSS) gem5 is a popular choice for MPSoC design space exploration, thanks to its flexibility and robust set of detailed timing models. However, its single-threaded simulation kernel severely hampers its throughput. To address this challenge, we introduce parti-gem5, an extension of gem5 that enables parallel timing simulations on modern multi-core simulation hosts. Unlike previous works, parti-gem5 supports gem5’s timing mode, the O3CPU, and Ruby’s custom cache and interconnect models. Compared to reference single-thread simulations, we achieved speedups of up to 42.7× when simulating a 120-core ARM MPSoC on a 64-core x86-64 host system. While our method introduces timing deviations, the error in total simulated time is below 15% in most cases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Timing circuits

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Advanced gate-level glitch modeling using ANNs 24

Advanced gate-level glitch modeling using ANNs

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61st ACM/IEEE Design Automation conference, DAC 2024

作者： Vagenas, Anastasis Garyfallou, Dimitrios Evmorfopoulos, Nestor Stamoulis, George Department of Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (纸本)9798400706011

Multiple Input Switching (MIS) effects commonly induce undesired glitch pulses at the output of CMOS gates, potentially leading to circuit malfunction and significant power consumption. Thus, accurate and efficient glitch modeling is crucial for the design of high-performance, low-power, and reliable ICs. In this work, we present a new gate-level approach for modeling glitch effects under MIS. Unlike previous studies, we leverage efficient Machine Learning (ML) techniques to accurately estimate the glitch shape characteristics, propagation delay, and power consumption. To this end, we evaluate various ML engines and explore different Artificial Neural Network (ANN) architectures. Moreover, we introduce a seamless workflow to integrate our ANNs into existing standard cell libraries, striking an optimal balance between model size and accuracy in gate-level glitch modeling. Experimental evaluation on gates implemented in 7 nm FinFET technology demonstrates that the proposed models achieve an average error of 2.19% against SPICE simulation while maintaining a minimal memory footprint. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： SPICE

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A Tale of Two Domains: Exploring Efficient Architecture Design for Truly Autonomous Things 51

A Tale of Two Domains: Exploring Efficient Architecture Desi...

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ACM/IEEE 51st Annual international Symposium on computer Architecture (ISCA)

作者： Hou, Xiaofeng Xu, Tongqiao Li, Chao Xu, Cheng Liu, Jiacheng Hu, Yang Zhao, Jieru Leng, Jingwen Cheng, Kwang-Ting Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China CUHK Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China HKUST Hong Kong Peoples R China

ISBN: (纸本)9798350326598;9798350326581

Autonomous Things (AuT) refers to a collection of self-sufficient tiny devices capable of performing intelligent computations. Looking ahead, AuT promises to enable ubiquitous deployment of intelligence on many emerging consumer electronics and mission-critical infrastructures. Nevertheless, there is an important research gap to date: architecting efficient AuT systems requires both energy autonomy (EA) and inference autonomy (IA). In other words, practical AuT application scenarios necessitate tailored architectures with significantly expanded inference performance and more efficient use of energy. We present CHRYSALIS, a novel automated EA/IA co-design methodology for autonomous things. It aims to guide the transition from a traditional EA-only and IA-only design approach to a truly AuT-oriented architecture design. To fully understand the interrelationship between the EA domain and the IA domain, CHRYSALIS first introduces an architectural modeling framework encompassing every key AuT module involving energy harvesting, intermittent execution, and accelerator control. Based on the holistic system model, we design an intelligent architecture generation tool that can help find the ideal design for targeted AuT scenarios adhering to different SWaP (Size, Weight and Power) constraints. To validate our work, we use CHRYSALIS for fast construction and exploration of efficient AuT design and pre-RTL design in representative AuT scenarios. Extensive evaluation shows that CHRYSALIS outperforms state-of-the-art designs and our proposed technique shows 56.4% better performance on average. We believe that the methodology and tools developed in this paper will foster the development of more performant and practical architectures in the upcoming AuT era.

关键词： Edge Artificial Intelligence Autonomous embedded systems Intermittent Computing Accelerator Deep Learning

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The Impact of Dynamic Storage Allocation on CPython Execution Time, Memory Footprint and Energy Consumption: An Empirical Study 22nd

The Impact of Dynamic Storage Allocation on CPython Executio...

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22nd international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Lamprakos, Christos P. Papadopoulos, Lazaros Catthoor, Francky Soudris, Dimitrios Natl Tech Univ Athens Iroon Polytech St 9 Zografos 15780 Greece Katholieke Univ Leuven Oude Markt 13 B-3000 Leuven Belgium IMEC Sci PkGaston Geenslaan 14 B-3001 Leuven Belgium

ISBN: (纸本)9783031150746;9783031150739

CPython is the reference implementation of the Python programming language. Tools like machine learning frameworks, web development interfaces and scientific computing libraries have been built on top of it. Meanwhile, single-board computers are now able to run GNU/Linux distributions. As a result CPython's influence today is not limited to commodity servers, but also includes edge and mobile devices. We should thus be concerned with the performance of CPython applications. In this spirit, we investigate the impact of dynamic storage allocation on the execution time, memory footprint and energy consumption of CPython programs. Our findings show that (i) CPython's default configuration is optimized for memory footprint, (ii) replacing this configuration can improve performance by more than 1.6x and (iii) application-specific characteristics define which allocator setup performs best at each case. Additionally, we contribute an open-source means for benchmarking the energy consumption of CPython applications. By employing a rigorous and reliable statistical analysis technique, we provide strong indicators that most of our conclusions are platform-independent.

关键词： CPython Dynamic storage allocation embedded systems Energy efficiency Benchmarking studies

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Proceedings of the international conference on Application-Specific systems, architectures and Processors

Proceedings of the International Conference on Application-S...

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34th IEEE international conference on Application-Specific systems, architectures and Processors, ASAP 2023

ISBN: (纸本)9798350346855

The proceedings contain 30 papers. The topics discussed include: exploiting subword permutations to maximize CNN compute performance and efficiency;co-design of algorithm and FPGA accelerator for conditional independence test;real-time and approximate iterative optical flow implementation on low-power embedded CPUs;FMM-X3D: FPGA-based modeling and mapping of X3D for human action recognition;a heterogeneous computer architecture accelerating reinforcement learning-based design for silicon photonic devices;MAYALOK: a cyber-deception hardware using runtime instruction infusion;supporting RISC-V performance counters through Linux performance analysis tools;iKnowFirst: an efficient DPU-assisted compaction for LSM-tree-based key-value stores;and GPU acceleration of multi-object tracking with motion vector interpolation and affine transformation.

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Run-Time Detection of Malicious Behavior Based on Exploit Decomposition Using Deep Learning: A Feasibility Study on SysJoker 23rd

Run-Time Detection of Malicious Behavior Based on Exploit De...

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23rd international conference on embedded computer systems: architectures, modeling, and simulation, SAMOS 2023

作者： Tsakoulis, Thanasis Haleplidis, Evangelos Fournaris, Apostolos P. Industrial Systems Institute Research Center ATHENA Patra Greece Department of Digital Systems University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Patras Patras Greece

ISBN: (纸本)9783031460760

As malicious operations become gradually very complex and involve advanced attack campaigns even on embedded systems and IoT, there is an increasing need for detecting malicious behavior on a system as it dynamically as it happens. In this paper, a Deep Learning based malicious behavior dynamic, run-time detection methodology is proposed that rely on the collection of Linux OS execution flow metrics/features like CPU, disk and memory usage and their association with ATT&CK MITRE knowledge base exploits. Using that approach, we can emulate the attack sequence of complex malicious activity and use the collected features to train Deep Learning models in order to classify execution operations at run-time as malicious or not. In the paper, we provide a feasibility study of the proposed solution based on the ATT&CK MITRE exploit attack graph emulation of the SysJoker backdoor malware and we train several Deep Learning models acting as classifiers. The provided results showed that in SysJoker use-case study of the proposed approach we managed to obtain more than 99% accuracy and less that 0.5% False Positive and False Negative Rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： embedded systems

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