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Hardware-irrelevant parallel processing system [article]

Xiuting Zou, Shaofu Xu, Anyi Deng, Rui Wang, Weiwen Zou
2020 arXiv   pre-print
We propose a hardware-irrelevant PPS of which the performance is unaffected by hardware deviations.  ...  mismatch degrees under random system states.  ...  random hardware deviations.  ... 
arXiv:2006.13443v1 fatcat:f4zqcqznljecfl6llix3vvbngy

Hardware-Constrained Millimeter Wave Systems for 5G: Challenges, Opportunities, and Solutions [article]

Xi Yang, Michail Matthaiou, Jie Yang, Chao-Kai Wen, Feifei Gao, and Shi Jin
2018 arXiv   pre-print
Although millimeter wave (mmWave) systems promise to offer larger bandwidth and unprecedented peak data rates, their practical implementation faces several hardware challenges compared to sub-6 GHz communication  ...  These hardware constraints can seriously undermine the performance and deployment progress of mmWave systems and, thus, necessitate disruptive solutions in the cross-design of analog and digital modules  ...  relieve the analog-digital interface from the ever increasing data processing requirements. 2) By cooperating with the hardware assistant module, the RF front ends of mmWave systems can avail of better  ... 
arXiv:1811.03269v1 fatcat:vn2zt43hlrelhgfjum5eguecxe

ArduCode: Predictive Framework for Automation Engineering [article]

Arquimedes Canedo and Palash Goyal and Di Huang and Amit Pandey and Gustavo Quiros
2020 arXiv   pre-print
We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators  ...  Finally, we use autoencoder models for hardware recommendation and achieve a p@3 of 0.79 and p@5 of 0.95.  ...  We compare two approaches for the hardware recommendation task. Our baseline consists of the predictions given on random hardware configurations.  ... 
arXiv:1909.04503v4 fatcat:2kfya3t7knhl7bdqqvrg6ke4km

Hash Attacks Prevention for Instruction Security in Embedded Monitoring System

Xiang WANG, Zhan-Hong HE, Yang XU, Shu-Song PANG, Xiao-Cui WANG, Cheng ZHOU, Pei DU
2016 DEStech Transactions on Engineering and Technology Research  
Embedded security monitoring module is a dedicated hardware that runs parallel with the embedded processor, which is used to monitor the integrity of the data and code to enhance program execution security  ...  It uses hardware-supported methods computing a hash value of instructions with the hash algorithm as an official reference value to prevent malicious attacks on the program code.  ...  Fig.2 shows the block diagram of the proposed hardware assisted monitoring architecture.  ... 
doi:10.12783/dtetr/ssme-ist2016/4025 fatcat:hosfchnfyrattcllwem6gtgsmm

The Effects of Cloud Computing (IaaS) on E- Libraries in United Arab Emirates

Mounir Mohamed El Khatib, Maria Jade Catalan Opulencia
2015 Procedia Economics and Finance  
Further ,the paper focused in depth business case that looked into preserving data in the cloud to assist the work flow in the libraries and eased the tasks of the librarians .  ...  infrastructure (IaaS) to juggle all the accumulative work of the hardware.  ...  Clearly as a result, 83% collectively agreed that some data should remained untouched by random users.  ... 
doi:10.1016/s2212-5671(15)00521-3 fatcat:gogub2gnwjdzli6kwpr2mkefii

Hardware assisted control flow obfuscation for embedded processors

Xiaotong Zhuang, Tao Zhang, Hsien-Hsin S. Lee, Santosh Pande
2004 Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems - CASES '04  
To address all of these shortcomings, this paper presents a hardware assisted obfuscation technique that is capable of obfuscating the control flow information dynamically.  ...  Finally, we show that our scheme can be implemented on embedded systems with very little hardware overhead.  ...  Based on existing hardware encryption techniques, hardware assisted control flow obfuscation provides a much higher level of obfuscation than its software-based counterpart.  ... 
doi:10.1145/1023833.1023873 dblp:conf/cases/ZhuangZLP04 fatcat:5qhm2pkcpzf35a5plsryifryba

Revizor: Testing Black-box CPUs against Speculation Contracts [article]

Oleksii Oleksenko, Christof Fetzer, Boris Köpf, Mark Silberstein
2021 arXiv   pre-print
Revizor automatically detects violations of a rich set of contracts, or indicates their absence.  ...  Such vulnerabilities often stay undetected for a long time as we lack the tools for systematic testing of CPUs to find them.  ...  . • In *+Assist mode, the executor includes microcode assists. It clears the "Accessed" bit in one of the accessible pages such that the first store or load triggers an assist 2 .  ... 
arXiv:2105.06872v2 fatcat:ul5ljrrk3zdupp2j53a62orw5u

In-Memory Data Rearrangement for Irregular, Data-Intensive Computing

Scott Lloyd, Maya Gokhale
2015 Computer  
Using a custom FPGA emulator, we quantitatively evaluate performance and energy benefits of near-memory hardware structures that dynamically restructure in-memory data to cache-friendly layout, minimizing  ...  Unlike other proposed processingin-memory architectures, the rearrangement hardware performs data reduction, not compute offload.  ...  Acknowledgments This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344.  ... 
doi:10.1109/mc.2015.230 fatcat:56h5aajgz5dh3d4wvfm5rg44oe

TinyDL: Just-in-time deep learning solution for constrained embedded systems

Bita Darvish Rouhani, Azalia Mirhoseini, Farinaz Koushanfar
2017 2017 IEEE International Symposium on Circuits and Systems (ISCAS)  
Thesis Award, Sharif University, 2013 Professional Research Intern Summer 2016 Graduate Research Assistant 2013-Present • University of California, San Diego • Rice University Teaching Assistant  ...  Mining and Statistical Learning, Algorithms, Parallel Programming, Computational Science, Advanced Digital Hardware Designs, Signals and Systems, Digital Signal Processing, Random Processes, Computer  ... 
doi:10.1109/iscas.2017.8050343 dblp:conf/iscas/RouhaniMK17 fatcat:k3fupgklvfbbzomljoeyb3wpea

Medical image classification via quantum neural networks [article]

Natansh Mathur, Jonas Landman, Yun Yvonna Li, Martin Strahm, Skander Kazdaghli, Anupam Prakash, Iordanis Kerenidis
2021 arXiv   pre-print
The results show the promises of such techniques and the limitations of current quantum hardware.  ...  Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification.  ...  We acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team.  ... 
arXiv:2109.01831v1 fatcat:uobfchpv5bagtauaofyt6lhlku

Preventing kernel code-reuse attacks through disclosure resistant code diversification

Jason Gionta, William Enck, Per Larsen
2016 2016 IEEE Conference on Communications and Network Security (CNS)  
Unfortunately, memory disclosure vulnerabilities assist adversaries in bypassing software diversity protections by leaking the code layout.  ...  We provide a security analysis of KHide calculating the survivability of gadgets across diversified versions.  ...  This can be realized through Hardware Assisted Paging on x86 or on ARM. Fig. 1 : Overview of KHide.  ... 
doi:10.1109/cns.2016.7860485 dblp:conf/cns/GiontaEL16 fatcat:nh4uohrjybdv5nlz5awsnqv4vq

Beamforming and Performance Evaluation for Intelligent Reflecting Surface Aided Wireless System with Hardware Impairments [article]

Yiming Liu, Erwu Liu, Rui Wang
2021 arXiv   pre-print
To the best of our knowledge, it is the first research comprehensively evaluating the impact of hardware impairments on IRS-assisted wireless systems.  ...  First, we characterize the closed-form estimators of direct and cascade channels in both cases of single-user and multi-user and analyze the impact of hardware impairments on channel estimation accuracy  ...  The phase errors of reflecting elements caused by hardware impairments on IRS are random and unknown to the BS in practice.  ... 
arXiv:2006.00664v2 fatcat:dln2igpdk5flfiff3qiozajryy

Channel Estimation and Power Scaling Law of Large Reflecting Surface with Non-Ideal Hardware [article]

Yiming Liu, Erwu Liu, Rui Wang, Yuanzhe Geng
2020 arXiv   pre-print
In this paper, we consider an LRS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis.  ...  Most existing studies were conducted with an assumption of ideal hardware, and the impact of hardware impairments receives little attention.  ...  CONCLUSION In this paper, we study the LRS-assisted communication system by considering hardware impairments.  ... 
arXiv:2004.09761v1 fatcat:pv5vwuxpgrgj7gcz7u5ip4qpni

Creating the Capacity for Telephone Survey Analysis

Lani Lee Malysa
1998 PS: Political Science and Politics  
Additionally, I will discuss computer hardware and software requirements, purchase of random telephone number samples, personnel issues, and security issues.  ...  Costs associated with performing a telephone survey include: paying overhead (includes use of survey analysis facility and computer hardware); purchasing random telephone numbers from a sampling company  ... 
doi:10.1017/s1049096500053464 fatcat:q5ilxpuaqfgvbillnlvqziyxkq

Memory Access Pattern Protection for Resource-Constrained Devices [chapter]

Yuto Nakano, Carlos Cid, Shinsaku Kiyomoto, Yutaka Miyake
2013 Lecture Notes in Computer Science  
We first consider an instance which relies on the use of a secure (trusted) hardware buffer; it achieves both security and performance levels acceptable in practice by adapting ideas from oblivious RAM  ...  Another instance requires no special hardware, but as a result leads to a higher, yet practical overhead.  ...  However large M and H negatively affect the performance of the scheme (as well as increase its costs in the hardware-assisted version).  ... 
doi:10.1007/978-3-642-37288-9_13 fatcat:rtxlaojg6begffif75jk5y2r4e
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