Academic literature on the topic 'Kernel architecture'

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Journal articles on the topic "Kernel architecture"

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Torres-Huitzil, Cesar. "Resource Efficient Hardware Architecture for Fast Computation of Running Max/Min Filters." Scientific World Journal 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/108103.

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Running max/min filters on rectangular kernels are widely used in many digital signal and image processing applications. Filtering with ak×kkernel requires ofk2−1comparisons per sample for a direct implementation; thus, performance scales expensively with the kernel sizek. Faster computations can be achieved by kernel decomposition and using constant time one-dimensional algorithms on custom hardware. This paper presents a hardware architecture for real-time computation of running max/min filters based on the van Herk/Gil-Werman (HGW) algorithm. The proposed architecture design uses less compu
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Giannoula, Christina, Ivan Fernandez, Juan Gómez-Luna, Nectarios Koziris, Georgios Goumas, and Onur Mutlu. "Towards Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 33–34. http://dx.doi.org/10.1145/3547353.3522661.

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Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks. Recent research demonstrates that they can yield significant performance and energy improvements in parallel applications by alleviating data access costs. Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the Sparse Matrix Vector Multiplication (SpMV) kernel. SpMV has been
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Muhammad, Ali, Weicheng Hu, Zhaoyang Li, et al. "Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS." International Journal of Molecular Sciences 21, no. 16 (2020): 5649. http://dx.doi.org/10.3390/ijms21165649.

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Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the
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Patel, Chirag, Dulari Bhatt, Urvashi Sharma, et al. "DBGC: Dimension-Based Generic Convolution Block for Object Recognition." Sensors 22, no. 5 (2022): 1780. http://dx.doi.org/10.3390/s22051780.

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The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities,
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Kumar, Anish. "Linux Kernel Input Subsystem: Architecture and Programming Interface." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 1852–54. http://dx.doi.org/10.21275/sr230311123408.

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Solomon, D. A. "The Windows NT kernel architecture." Computer 31, no. 10 (1998): 40–47. http://dx.doi.org/10.1109/2.722284.

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Zhou, Xiaojian, Qianqian Geng, and Ting Jiang. "Boosting RBFNN performance in regression tasks with quantum kernel methods." Journal of Statistical Mechanics: Theory and Experiment 2025, no. 6 (2025): 063101. https://doi.org/10.1088/1742-5468/add0a3.

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Abstract Quantum and classical machine learning are fundamentally connected through kernel methods, with kernels serving as inner products of feature vectors in high-dimensional spaces, forming their foundation. Among commonly used kernels, the Gaussian kernel plays a prominent role in radial basis function neural network (RBFNN) for regression tasks. Nonetheless, the localized response property of the Gaussian kernel, which emphasizes relationships between nearby data points, limits its capacity to model interactions among more distant data points. As a result, it may potentially overlook the
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Ganjdanesh, Alireza, Shangqian Gao, and Heng Huang. "EffConv: Efficient Learning of Kernel Sizes for Convolution Layers of CNNs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7604–12. http://dx.doi.org/10.1609/aaai.v37i6.25923.

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Determining kernel sizes of a CNN model is a crucial and non-trivial design choice and significantly impacts its performance. The majority of kernel size design methods rely on complex heuristic tricks or leverage neural architecture search that requires extreme computational resources. Thus, learning kernel sizes, using methods such as modeling kernels as a combination of basis functions, jointly with the model weights has been proposed as a workaround. However, previous methods cannot achieve satisfactory results or are inefficient for large-scale datasets. To fill this gap, we design a nove
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Liu, Dake, Joar Sohl, and Jian Wang. "Parallel Programming and Its Architectures Based on Data Access Separated Algorithm Kernels." International Journal of Embedded and Real-Time Communication Systems 1, no. 1 (2010): 64–85. http://dx.doi.org/10.4018/jertcs.2010103004.

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A novel master-multi-SIMD architecture and its kernel (template) based parallel programming flow is introduced as a parallel signal processing platform. The name of the platform is ePUMA (embedded Parallel DSP processor architecture with Unique Memory Access). The essential technology is to separate data accessing kernels from arithmetic computing kernels so that the run-time cost of data access can be minimized by running it in parallel with algorithm computing. The SIMD memory subsystem architecture based on the proposed flow dramatically improves the total computing performance. The hardwar
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Qureshi, Yasir Mahmood, William Andrew Simon, Marina Zapater, Katzalin Olcoz, and David Atienza. "Gem5-X." ACM Transactions on Architecture and Code Optimization 18, no. 4 (2021): 1–27. http://dx.doi.org/10.1145/3461662.

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The increasing adoption of smart systems in our daily life has led to the development of new applications with varying performance and energy constraints, and suitable computing architectures need to be developed for these new applications. In this article, we present gem5-X, a system-level simulation framework, based on gem-5, for architectural exploration of heterogeneous many-core systems. To demonstrate the capabilities of gem5-X, real-time video analytics is used as a case-study. It is composed of two kernels, namely, video encoding and image classification using convolutional neural netw
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Dissertations / Theses on the topic "Kernel architecture"

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Preney, Paul David. "Towards a kernel-architecture for modelling systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0018/MQ52639.pdf.

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Forestan, Cristian. "ZmPIN Genes And Polar Auxin Transport In Maize: Roles on Kernel Development & Plant Architecture Determination." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3425672.

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Due to the unquestionable importance of the polar auxin transport in controlling a multitude of developmental processes in plants, several research projects are ongoing towards a better understanding of the genetic regulation and physiology of auxin mediated?morphogenesis. Several components of the auxin transport machinery, and their regulatory networks, have been identified in the model specie Arabidopsis thaliana, shedding light on the molecular and cytological mechanism underlying this unique phenomenon for transmitting spatial and temporal signals in plant development. Evolutionary
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Menon, Suraj S. "Supporting Distributed Fault Tolerance In A Real-Time Micro-Kernel." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/35463.

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Research into modular approaches for constructing power electronics control systems has provided a number of benefits, as well as new opportunities. Control systems composed of an interconnected collection of standardized parts makes distributed processing a realistic possibility. Unfortunately, current strategies to supporting software on such systems have a number of critical drawbacks. Many existing approaches rely on centralized control strategies, fail to support fault tolerance in the face of failures among processing nodes or communications links, and fail to robustly support live add
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Costa, Celso Maciel da. "Environnement d'exécution parallèle : conception et architecture." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1993. http://hdl.handle.net/10183/18239.

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L'objectif de cette thèse est l'étude d'un environnement d'exécution pour machines parallèles sans mémoire commune. Elle comprend la définition d'un modèle de programme parallèle, basé sur l'échange de message offrant une forme restreinte de mémoire partagée. La communication est indirecte, via des portes; les processus utilisent les barrières pour la synchronisation. Les entités du système. processus, portes et barrières, sont créées dynamiquement, et placées sur un processeur quelconque du réseau de processeurs de façon explicite. Nous proposons une implantation de ce modèle comme la mise en
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Javaid, Salman. "Analysis and Detection of Heap-based Malwares Using Introspection in a Virtualized Environment." ScholarWorks@UNO, 2014. http://scholarworks.uno.edu/td/1875.

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Malware detection and analysis is a major part of computer security. There is an arm race between security experts and malware developers to develop various techniques to secure computer systems and to find ways to circumvent these security methods. In recent years process heap-based attacks have increased significantly. These attacks exploit the system under attack via the heap, typically by using a heap spraying attack. The main drawback with existing techniques is that they either consume too many resources or are complicated to implement. Our work in this thesis focuses on new methods whic
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Evgeniou, Theodoros K. (Theodoros Kostantinos) 1974. "Learning with kernel machine architectures." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86442.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 99-106).<br>by Theodoros K. Evgeniou.<br>Ph.D.
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Vasisht, Vikas R. "Architectural support for autonomic protection against stealth by rootkit exploits." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26618.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Lee, Hsien-Hsin; Committee Member: Blough, Douglas; Committee Member: Copeland, John. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Venkatasubramanian, Sundaresan. "Tuned and asynchronous stencil kernels for CPU/GPU systems." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29728.

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Thesis (M. S.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Vuduc, Richard; Committee Member: Kim, Hyesoon; Committee Member: Vetter, Jeffrey. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Srivastava, Abhinav. "Robust and secure monitoring and attribution of malicious behaviors." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41161.

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Worldwide computer systems continue to execute malicious software that degrades the systemsâ performance and consumes network capacity by generating high volumes of unwanted traffic. Network-based detectors can effectively identify machines participating in the ongoing attacks by monitoring the traffic to and from the systems. But, network detection alone is not enough; it does not improve the operation of the Internet or the health of other machines connected to the network. We must identify malicious code running on infected systems, participating in global attack networks. This dissertat
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Krishnaswami, Sreedhar Bharathwaj. "Bayesian Optimization for Neural Architecture Search using Graph Kernels." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291219.

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Neural architecture search is a popular method for automating architecture design. Bayesian optimization is a widely used approach for hyper-parameter optimization and can estimate a function with limited samples. However, Bayesian optimization methods are not preferred for architecture search as it expects vector inputs while graphs are high dimensional data. This thesis presents a Bayesian approach with Gaussian priors that use graph kernels specifically targeted to work in the higherdimensional graph space. We implemented three different graph kernels and show that on the NAS-Bench-101 data
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Books on the topic "Kernel architecture"

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Mauerer, Wolfgang. Professional Linux Kernel Architecture. John Wiley & Sons, Ltd., 2008.

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Mauerer, Wolfgang. Professional Linux kernel architecture. Wiley Pub., 2008.

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Jim, Mauro, and Mauro Jim, eds. Solaris internals: Solaris 10 and OpenSolaris kernel architecture. 2nd ed. Sun Microsystems Press/Prentice Hall, 2007.

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Schimmel, Curt. UNIX systems for modern architectures: Symmetric multiprocesssing and caching for kernel programmers. Addison-Wesley, 1994.

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1972-, Wehrle Klaus, ed. The Linux networking architecture: Design and implementation of network protocols in the Linux kernel. Pearson Prentice Hall, 2004.

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McKendry, Martin S. A support architecture for reliable distributed computing systems: Interim technical report, November 9, 1983-December 3, 1985. Georgia Institute of Technology, School of Information and Computer Science, 1986.

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Mehendale, Mahesh. VLSI synthesis of DSP kernels: Algorithmic and architectural transformations. Kluwer Academic Publishers, 2001.

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Mehendale, Manesh. VLSI Synthesis of DSP Kernels: Algorithmic and Architectural Transformations. Springer US, 2001.

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D, Sherlekar Sunil, ed. VLSI synthesis of DSP kernels: Algorithmic and architectural transformations. Kluwer Academic Publishers, 2001.

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Halang, Wolfgang A. Definition of an auxiliary processor dedicated to real-time operating system kernels. Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1988.

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Book chapters on the topic "Kernel architecture"

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Paul, Wolfgang J., Christoph Baumann, Petro Lutsyk, and Sabine Schmaltz. "A Generic Operating System Kernel." In System Architecture. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43065-2_15.

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Haeberlen, Andreas, and Kevin Elphinstone. "User-Level Management of Kernel Memory." In Advances in Computer Systems Architecture. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39864-6_23.

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Oikawa, Shuichi. "Independent Kernel/Process Checkpointing on Non-Volatile Main Memory for Quick Kernel Rejuvenation." In Architecture of Computing Systems – ARCS 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04891-8_20.

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Hidaka, Yasuo, Hanpei Koike, and Hidehiko Tanaka. "Architecture of parallel management kernel for PIE64." In PARLE '92 Parallel Architectures and Languages Europe. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55599-4_118.

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Zhao, Mingwei, and Rongan Jiang. "An Improved Kernel Trojan Horse Architecture Model." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28798-5_18.

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Elwasif, Wael R., David E. Bernholdt, James A. Kohl, and G. A. Geist. "An Architecture for a Multi-threaded Harness Kernel." In Recent Advances in Parallel Virtual Machine and Message Passing Interface. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45417-9_21.

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Liu, Guojun, Jili Huang, and Xiaoyan Liu. "An OS Kernel Based on RISC-V Architecture." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9499-1_1.

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Rachid, Latif, Jahid Khadija, and Saddik Amine. "OpenCL Kernel Optimization Metrics for CPU-GPU Architecture." In Digital Technologies and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29857-8_77.

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Sandhya Devi, R. S., P. Sivakumar, and R. Balaji. "AUTOSAR Architecture Based Kernel Development for Automotive Application." In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03146-6_104.

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Torres, M., and W. V. Srubar. "Using kernel density estimation to model uncertainty in building material emissions: Considering variable weighting and bandwidths." In Structures and Architecture. CRC Press, 2025. https://doi.org/10.1201/9781003658641-73.

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Conference papers on the topic "Kernel architecture"

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Lo Presti, Nicolò, Kamilia Abahri, Giovanni Castellazzi, Paolo Mengoli, and Paolo Stabellini. "Experimental Investigation of Lightweight Mortars Based on Recycled Olive Kernel through Hygric and Mechanical Characterization." In 2024 10th International Conference on Architecture, Materials and Construction & 2024 5th International Conference on Building Science, Technology and Sustainability. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-z8fxbr.

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Bio-based building materials are now being widely explored by researchers to promote their effective use and reduce the environmental impact of building construction. Among these, mortars based on olive kernel aggregate, a waste product of the production of olive oil, can be effectively adopted for moderate load-bearing applications in buildings thanks to their lower density and thermal conductivity,compared to traditional sand-based mortars. Nevertheless, to date most of the researchers focused on mortars produced with a partial substitution of traditional sand with olive kernels and relative
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Monteiro, Marcio, Ismael Seidel, José Güntzel, Mateus Grellert, Leonardo Soares, and Cristina Meinhardt. "A Dual-Kernel Size-Configurable Gaussian Filter Architecture." In 2024 37th SBC/SBMicro/IEEE Symposium on Integrated Circuits and Systems Design (SBCCI). IEEE, 2024. http://dx.doi.org/10.1109/sbcci62366.2024.10703872.

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Chen, Po-Ting, Shan-Chi Yu, and Ing-Chao Lin. "An Efficient Sparse CNN Architecture with Index-based Kernel Transformation." In 2024 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS). IEEE, 2024. https://doi.org/10.1109/apccas62602.2024.10808381.

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Jiang, Yue, Wenjia Zhang, Han Wang, Jiayuan Guo, Jiangbing Du, and Zuyuan He. "Photonic 4D-Convolution Tensor Core based on Micro-Ring Mesh Architecture." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w3d.3.

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We propose a 4D-photonic convolution tensor core leveraged by the micro-ring mesh that enabling parallel batch processing and parallel multi-kernel computation. The equivalent computing power is up to 2.24 Tera-operations per second (TOPs) per second with over 90% recognition accuracy.
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Ma, Junchao, Yu Tian, Yanchang Feng, et al. "ZTKA: A Zero-Trust Based Kernel Encryption Architecture for Transparent Data Protection." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033649.

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Athalye, Anish, Frans Kaashoek, Nickolai Zeldovich, and Joseph Tassarotti. "The K2 Architecture for Trustworthy Hardware Security Modules." In KISV '23: 1st Workshop on Kernel Isolation, Safety and Verification. ACM, 2023. http://dx.doi.org/10.1145/3625275.3625402.

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De Oliveira, Lucas, Guilherme Mota, and Vitor Vidal. "A Thorough Evaluation of Kernel Order in CNN Based Traffic Signs Recognition." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13485.

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Convolutional Neural Network is an important deep learning architecture for computer vision. Alongside with its variations, it brought image analysis applications to a new performance level. However, despite its undoubted quality, the evaluation of the performance presented in the literature is mostly restricted to accuracy measurements. So, considering the stochastic characteristics of neural networks training and the impact of the architectures configuration, research is still needed to affirm if such architectures reached the optimal configuration for their focused problems. Statistical sig
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Nurnberger, Stefan, Thomas Feller, and Sorin A. Huss. "Ray - a secure micro kernel architecture." In 2010 Eighth Annual International Conference on Privacy, Security and Trust (PST). IEEE, 2010. http://dx.doi.org/10.1109/pst.2010.5593231.

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Li, Shaowen, and Hiroyuki Sato. "W-Kernel: An OS Kernel Architecture Designed With Isolation and Customizability." In ICSED 2023: 2023 5th International Conference on Software Engineering and Development. ACM, 2023. http://dx.doi.org/10.1145/3637792.3637796.

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Bernabeu-Auban, J. M., P. W. Hutto, M. Y. A. Khalidi, et al. "The architecture of Ra: a kernel for Clouds." In [1989] Proceedings of the Twenty-Second Annual Hawaii International Conference on System Sciences. Volume II: Software Track. IEEE Comput. Soc. Press, 1989. http://dx.doi.org/10.1109/hicss.1989.48105.

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Reports on the topic "Kernel architecture"

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Bamberger, Judy, Timothy Coddington, Currie Colket, Robert Firth, and Daniel Klein. Kernel Architecture Manual. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada219295.

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Lebak, J., A. Reuther, and E. Wong. Polymorphous Computing Architecture (PCA) Kernel-Level Benchmarks. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440246.

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Lebak, J., A. Reuther, and E. Wong. Polymorphous Computing Architecture (PCA) Kernel-Level Benchmarks. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada419630.

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Haney, R. J., J. M. Lebak, M. A. Alexander, H. Chan, P. A. Jackson, and E. L. Wong. Polymorphous Computing Architecture (PCA) Kernel Benchmark Measurements on the MIT Raw Microprocessor. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada453293.

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Xu, Dongyan, Eugene H. Spafford, and Xuxian Jiang. An Integrated Architecture for Automatic Indication, Avoidance and Profiling of Kernel Rootkit Attacks. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609410.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Kurzak, Jakub, Pitor Luszczek, Stanimire Tomov, and Jack Dongarra. Preliminary Results of Autotuning GEMM Kernels for the NVIDIA Kepler Architecture- GeForce GTX 680. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1173292.

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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