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

共 2875 条记录，以下是171-180 订阅

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Combining Task- and Data-Level Parallelism for High-Throughput CNN Inference on embedded CPUs-GPUs MPSoCs 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

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

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

Nowadays Convolutional Neural Networks (CNNs) are widely used to perform various tasks in areas such as computer vision or natural language processing. Some of the CNN applications require high-throughput execution of the CNN inference, on embedded devices, and many modern embedded devices are based on CPUs-GPUs multiprocessor systems-on-chip (MPSoCs). Ensuring high-throughput execution of the CNN inference on embedded CPUs-GPUs MPSoCs is a complex task, which requires efficient utilization of both task-level (pipeline) and data-level parallelism, available in a CNN. However, the existing Deep Learning frameworks utilize only task-level (pipeline) or only data-level parallelism, available in a CNN, and do not take full advantage of all embedded MPSoC computational resources. Therefore, in this paper, we propose a novel methodology for efficient execution of the CNN inference on embedded CPUs-GPUs MPSoCs. In our methodology, we ensure efficient utilization of both task-level (pipeline) and data-level parallelism, available in a CNN, to achieve high-throughput execution of the CNN inference on embedded CPUs-GPUs MPSoCs.

关键词： Convolutional Neural Networks Dataflow models SDF CSDF Mapping Hight throughput

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Enabling Power Side-Channel Attack simulation on Mixed-Signal Neural Network Accelerators

Enabling Power Side-Channel Attack Simulation on Mixed-Signa...

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Omni-layer Intelligent systems (COINS), international conference on

作者： Simon Wilhelmstätter Joschua Conrad Devanshi Upadhyaya Ilia Polian Maurits Ortmanns Institute of Microelectronics University of Ulm Ulm Germany Institute for Computer Architecture and Computer Engingeering University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350349597

ISBN: (纸本)9798350349603

Due to the tremendous success of Deep Learning with neural networks (NNs) in recent years and the simultaneous leap of embedded, low-power devices (e.g. wearables, smart-phones, IoT, and smart sensors), enabling the inference of those NNs in power-constrained environments gave rise to specialized NN accelerators. One paradigm followed by many of those accelerators was the transition from digital domain computing towards performing operations in the analog domain, turning them from digital to mixed-signal NN accelerators. While power-efficiency and inference accuracy have been researched with increasing interest, security and protection against a side-channel attack (SCA) have not found much attention. However, side-channels pose a major security concern by allowing an attacker to steal valuable knowledge about proprietary NNs deployed on accelerators. In order to evaluate mixed-signal NNs accelerators concerning SCA robustness, its tendency to leak information through the side-channel needs investigation. In this work, we propose a methodology for enabling side-channel analysis of mixed-signal NNs accelerators, which shows reasonable accuracy in an early development stage. The approach enables the reuse of large portions of design sources for simulation and production while providing flexibility and fast development cycles for changes to the analog design.

关键词： Accuracy Computational modeling Artificial neural networks Side-channel attacks Production Turning Robustness

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DRAMSys4.0: A Fast and Cycle-Accurate SystemC/TLM-Based DRAM Simulator 1

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Steiner, Lukas Jung, Matthias Prado, Felipe S. Bykov, Kirill Wehn, Norbert Tech Univ Kaiserslautern Kaiserslautern Germany Fraunhofer IESE Kaiserslautern Germany

ISBN: (数字)9783030609399

ISBN: (纸本)9783030609399;9783030609382

The simulation of DRAMs (Dynamic Random Access Memories) on system level requires highly accurate models due to their complex timing and power behavior. However, conventional cycle-accurate DRAM models often become the bottleneck for the overall simulation speed. A promising alternative are DRAM simulation models based on Transaction Level modeling, which can be fast and accurate at the same time. In this paper we present DRAMSys4.0, which is, to the best of our knowledge, the fastest cycle-accurate open-source DRAM simulator and has a large range of functionalities. DRAMSys4.0 includes a novel simulator architecture that enables a fast adaptation to new DRAM standards using a Domain Specific Language. We present optimization techniques to achieve a high simulation speed while maintaining full temporal accuracy. Finally, we provide a detailed survey and comparison of the most prominent cycle-accurate open-source DRAM simulators with regard to their supported features, analysis capabilities and simulation speed.

关键词： DRAM simulation SystemC TLM

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Adaptive Modulation and Coding in 5G Networks with Deep Learning

Adaptive Modulation and Coding in 5G Networks with Deep Lear...

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Communication, Computing and Signal Processing (IICCCS), IEEE international conference on

作者： Anjula Gupta Nirmala Reddy Ammar Hameed Shnain K.Revathi S.Gopalakrishnan Chetan K. Verma Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai Tamil Nadu India MBIT CVM University Gujrat India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

As part of the present work, it aims to examine the use of deep learning in the improvement of AMC in 5G networks. While traditional AMC methods like the ones mentioned above are applicable to previous generations of the mobile networks, they are not optimal taking into consideration the complexities and the constantly evolving nature of 5G systems. Therefore, we suggest the use of convolutional and recurrent neural networks with the aim of employing deep learning in designing an adaptive system that is capable of predicting the optimal modulation and coding schemes given instantaneous CSI information. It covers data gathering, process modeling, and implementation on a platform designed for 5G consumer network. Thus, the improvement in the data throughput rate, the level of errors, and the degree of satisfaction, obtained through the experimental results, can confirm the optimal nature of the proposed approach. It can be seen that the deep learning-based AMC system is computationally efficient and can be embedded for real-time applications, and the performance of the system is further optimised with improving learning. To conclude, this study reveals the effectiveness of deep learning in transforming AMC in 5G networks, by offering a dependable and dynamic solution the wired and wireless telecommunications networks. In a future work more towards detailing of the model and finding new architectures for the system more refined system can be achieved.

关键词： Deep learning Wireless communication Adaptation models Adaptive systems 5G mobile communication Computational modeling Modulation Throughput Encoding Computational efficiency

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Estimate the Efficiency of Multiprocessor's Cash Memory Work Algorithms

Estimate the Efficiency of Multiprocessor's Cash Memory Work...

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2021 IEEE international conference on Smart Information systems and Technologies, SIST 2021

作者： Hamada, Mohamed A. Abdallah, Abdelrahman International It University Department of Information System Kazakhstan Satbayev University Department of Machine Learning and Data Science Kazakhstan

ISBN: (纸本)9781728174709

Many computer systems for calculating the proper organization of memory are among the most critical issues. Using a tier cache memory (along with branching prediction) is an effective means of increasing modern multi-core processors' performance. Designing high-performance processors is a complex task and requires preliminary verification and analysis of the model level, usually used in analytical and simulation modeling. The refinement of extreme programming is an unfortunate challenge. Few experts disagree with the synthesis of access points. This article demonstrates that Internet QoS and 16-bit architectures are always incompatible, but it's the same situation for write-back caches. The solution to this problem can be implemented by analyzing simulation models of different complexity in combination with the analytical evaluation of individual algorithms. This work is devoted to designing a multi-parameter simulation model of a multi-process for evaluating the performance of cache memory algorithms and the optimality of the structure. Optimization of the structures and algorithms of the cache memory allows you to accelerate the interaction of the memory process and improve the performance of the entire system. © 2021 IEEE.

关键词： Wireless communication Analytical models Program processors Cache memory Reinforcement learning Quality of service Organizations

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VGM-Bench: FPU Benchmark Suite for computer Vision, computer Graphics and Machine Learning Applications 20th

VGM-Bench: FPU Benchmark Suite for Computer Vision, Computer...

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20th international conference on embedded computer systems - architectures, modeling, and simulation (SAMOS)

作者： Cremona, Luca Fornaciari, William Galimberti, Andrea Romanoni, Andrea Zoni, Davide Politecn Milan P zza Leonardo Da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9783030609399;9783030609382

With the Internet-of-things revolution, embedded devices are in charge of an ever increasing number of tasks ranging from sensing, up to Artificial Intelligence (AI) functions. In particular, AI is gaining importance since it can dramatically improve the QoS perceived by the final user and it allows to cope with problems whose algorithmic solution is hard to find. However, the associated computational requirements, mostly made of floating-point processing, impose a careful design and tuning of the computing platforms. In this scenario, there is a need for a set of benchmarks representative of the emerging AI applications and useful to compare the efficiency of different architectural solutions and computing platforms. In this paper we present a suite of benchmarks encompassing computer Graphics, computer Vision and Machine Learning applications, which are greatly used in many AI scenarios. Such benchmarks, differently from other suites, are kernels tailored to be effectively executed in bare-metal and specifically stress the floating-point support offered by the computing platform.

关键词： Benchmarks Machine learning Artificial intelligence Floating-point FPU

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Numerical Analysis of Bi-fluid PV/T Hybrid Collector Using the Finite Difference Method 6th

Numerical Analysis of Bi-fluid PV/T Hybrid Collector Using t...

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6th international conference on Wireless Technologies, embedded and Intelligent systems, WITS 2020

作者： El Manssouri, Oussama Hajji, Bekkay Gagliano, Antonio Tina, Giuseppe Marco Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco DIEEI: Department of Electric Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789813368927

The PV/T is called as a hybrid bi-fluid type solar collector when both fluids (water and air) are used as working fluids. This type of collector provide a wide range of thermal applications and several modes can be executed depending on energy needs and applications: air mode, water mode and simultaneous mode (air and water). This paper presents a numerical analysis of PV/T bi-fluid collector using the finite difference method. Energy balance equations were established for each layer and solved using the Gauss–Seidel iteration method. The performance of the PV/T hybrid is evaluated according to the operating mode of the fluid (independently and simultaneously). The simulation results are compared with literature (modeling and performances assessments of PV/T bifluid hybrid collector: Three cooling modes operation case. https://***/10.1109/iceit48248.2020.9113233, [10]), and indicate that when both fluids are used simultaneously, the overall electrical and thermal performance of the hybrid collector is considered satisfactory compared to the situation where two fluids operate independently. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Finite difference method

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A Dual-Critic Deep Deterministic Policy Gradient Approach for Task Offloading in Edge-Fog-Cloud Environment

A Dual-Critic Deep Deterministic Policy Gradient Approach fo...

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international conference on computer and Applications (ICCA)

作者： Moshira A. Ebrahim Gamal A. Ebrahim Hoda K. Mohamed Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt

Computation intensive IoT applications have grown exponentially over the past few years, which has led to the rise in popularity of the edge, fog, and cloud computing paradigms among mobile devices with embedded smart chips. Mobile Edge Computing (MEC) and Fog Computing (FC) usually cooperate with cloud computing to fulfill the high-demand requirements and optimize the network resources of complex computational offloading environments. In this paper, the topic of computational offloading for the hierarchical Edge-Fog-Cloud (EFC) computing is examined where edge, fog, and cloud nodes function as relay nodes that are constructed for latency critical and computations intensive jobs. Hierarchical EFC makes use of both centralized and distributed computing architectures considering diverse channel quality, network selection, and cloud access. This study proposes an improved Deep Reinforcement Learning (DRL) algorithm to find the optimal decision for offloading a computational task in EFC networks. simulation results validate the proposed DRL-based model by comparing it with other alternative algorithms. Experimental results demonstrate how the proposed approach can efficiently reduce the latency delay by 43% and energy cost by 32% of IoT end devices. Additionally, it can achieve a higher success rate compared with the other related algorithms with more than 16%.

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Adaptive Strategies for Maximizing Spectral Efficiency in dual branch MRC systems Facing non-Uniform Fading Patterns

Adaptive Strategies for Maximizing Spectral Efficiency in du...

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Rashmi Sharma Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India Dept. of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

The presented research paper investigates the development and performance of adaptive strategies for non-uniform fading patterns in dual-branch Maximal Ratio Combining systems. The study employs key enabling strategies, including power control, adaptive modulation, and antenna selection, with the purpose of improving the spectral efficiency and reliability of wireless communication systems. Conducting simulations in MATLAB and Python, this paper presents a comprehensive analysis using heatmaps, 3D surface plots, and dynamic simulation outputs to show the effect of the strategies under different Signal-to-Noise Ratios and fading conditions. The outcomes, illustrated in a clear-cut manner in the visuals, reveal that the adaptive strategies lead to a drastic reduction in the Bit Error Rates and a more optimal distribution of power across the branches, ensuring highly effective performance even in the case of the most challenging non-uniform fading patterns. Ultimately, the research findings show sight into the necessity of adaptive strategies to ensure proper coverage and quality of communication in non-uniform fading conditions, which novel research could benefit from.

关键词： Fading channels Solid modeling Analytical models Adaptive systems Three-dimensional displays Spectral efficiency Power control Bit error rate Modulation Signal to noise ratio

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Enhancing Video Protection: A Reversible Digital Watermarking approach using Swin features and Arnold Transform enabled Multi-Aggregation Model

Enhancing Video Protection: A Reversible Digital Watermarkin...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Mali Satish Dilip Agilandeeswari L School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Nowadays, the widespread internet technology brings out rapid advancement in the use of videos for sharing huge amounts of secret data. Conversely, the privacy of transmitted digital content is jeopardized by digital image processing techniques that are capable of producing flawless image or video duplication. Moreover, providing security to digital records in the form of videos is a highly challenging task, which requires a fusion-based watermarking technique to protect the data from illegitimate access and fraudulent usage. In this investigation, a tiny Swin and Arnold Transform-enabled Multi-aggregation model was introduced to protect video based on digital watermarking characteristics at a very low computational cost. Here, the Swin-T features are extracted from the input video, over which the Arnold transform and multi-aggregation model is developed in two phases. The best embedding location is identified by using multi aggregation model initially at the embedding phase and the inverse Arnold transform for secret image (VIT Logo) embedding in a binary embedding location to obtain the embedded video. The secret image is extracted from the attacked embedded video using the Arnold transform-multi-aggregation model in the extraction phase. The developed data-hiding technique is also evaluated to compute the robustness using the evaluation parameters. The simulation results reveal that the developed scheme achieved Peak-Signal-to-Noise Ratio (PSNR) and Similarity Index Measure (SSIM) values of $64.57 \mathrm{~dB}$ and 0.9993 respectively.

关键词： Privacy Computational modeling simulation Watermarking Transforms Feature extraction Transformers

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