Academic literature on the topic 'Data tightly coupled memory'

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Journal articles on the topic "Data tightly coupled memory"

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Kandiga, Bhadrayya Sowmya, and Ravishankar Vishwas Bangalore. "Central processing unit load reduction through application code optimization and memory management." International Journal of Reconfigurable and Embedded Systems (IJRES) 14, no. 1 (2025): 79–88. https://doi.org/10.11591/ijres.v14.i1.pp79-88.

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Central processing unit (CPU) loading refers to the amount of processing power a CPU uses to execute a given set of commands or perform an exact task. Higher CPU load can lead to slower, sluggish performance, reduced lifespan, and reduced system stability. Using the CPU Load trace results, the performance bottlenecks can be identified and suitable methods can be adopted to reduce the load on the CPU. For an ideal embedded system, the CPU should be in idle state for around 70% of CPU usage time. In this paper, three types of optimization techniques are implemented, which include application cod
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Bhadrayya, Sowmya Kandiga, and Vishwas Bangalore Ravishankar. "Central processing unit load reduction through application code optimization and memory management." International Journal of Reconfigurable and Embedded Systems (IJRES) 14, no. 1 (2025): 79. https://doi.org/10.11591/ijres.v14.i1.pp79-88.

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Central processing unit (CPU) loading refers to the amount of processing power a CPU uses to execute a given set of commands or perform an exact task. Higher CPU load can lead to slower, sluggish performance, reduced lifespan, and reduced system stability. Using the CPU Load trace results, the performance bottlenecks can be identified and suitable methods can be adopted to reduce the load on the CPU. For an ideal embedded system, the CPU should be in idle state for around 70% of CPU usage time. In this paper, three types of optimization techniques are implemented, which include application cod
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Witterauf, Michael, Dominik Walter, Frank Hannig, and Jürgen Teich. "Symbolic Loop Compilation for Tightly Coupled Processor Arrays." ACM Transactions on Embedded Computing Systems 20, no. 5 (2021): 1–31. http://dx.doi.org/10.1145/3466897.

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Tightly Coupled Processor Arrays (TCPAs), a class of massively parallel loop accelerators, allow applications to offload computationally expensive loops for improved performance and energy efficiency. To achieve these two goals, executing a loop on a TCPA requires an efficient generation of specific programs as well as other configuration data for each distinct combination of loop bounds and number of available processing elements (PEs). Since both these parameters are generally unknown at compile time—the number of available PEs due to dynamic resource management, and the loop bounds, because
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Mambu, Kévin, Henri-Pierre Charles, Maha Kooli, and Julie Dumas. "Towards Integration of a Dedicated Memory Controller and Its Instruction Set to Improve Performance of Systems Containing Computational SRAM." Journal of Low Power Electronics and Applications 12, no. 1 (2022): 18. http://dx.doi.org/10.3390/jlpea12010018.

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In-memory computing (IMC) aims to solve the performance gap between CPU and memories introduced by the memory wall. However, it does not address the energy wall problem caused by data transfer over memory hierarchies. This paper proposes the data-locality management unit (DMU) to efficiently transfer data from a DRAM memory to a computational SRAM (C-SRAM) memory allowing IMC operations. The DMU is tightly coupled within the C-SRAM and allows one to align the data structure in order to perform effective in-memory computation. We propose a dedicated instruction set within the DMU to issue data
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Laney, Daniel, Steven Langer, Christopher Weber, Peter Lindstrom, and Al Wegener. "Assessing the Effects of Data Compression in Simulations Using Physically Motivated Metrics." Scientific Programming 22, no. 2 (2014): 141–55. http://dx.doi.org/10.1155/2014/835419.

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This paper examines whether lossy compression can be used effectively in physics simulations as a possible strategy to combat the expected data-movement bottleneck in future high performance computing architectures. We show that, for the codes and simulations we tested, compression levels of 3–5X can be applied without causing significant changes to important physical quantities. Rather than applying signal processing error metrics, we utilize physics-based metrics appropriate for each code to assess the impact of compression. We evaluate three different simulation codes: a Lagrangian shock-hy
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Axinte, Cristian-Tiberius, Andrei Stan, and Vasile-Ion Manta. "Embedded Streaming Hardware Accelerators Interconnect Architectures and Latency Evaluation." Electronics 14, no. 8 (2025): 1513. https://doi.org/10.3390/electronics14081513.

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In the age of hardware accelerators, increasing pressure is applied on computer architects and hardware engineers to improve the balance between the cost and benefits of specialized computing units, in contrast to more general-purpose architectures. The first part of this study presents the embedded Streaming Hardware Accelerator (eSAC) architecture. This architecture can reduce the idle time of specialized logic. The remainder of this paper explores the integration of an eSAC into a Central Processing Unit (CPU) core embedded inside a System-on-Chip (SoC) design, using the AXI-Stream protocol
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García, Andrés Amaya, David May, and Ed Nutting. "Integrated Hardware Garbage Collection." ACM Transactions on Embedded Computing Systems 20, no. 5 (2021): 1–25. http://dx.doi.org/10.1145/3450147.

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Garbage collected programming languages, such as Python and C#, have accelerated software development. These modern languages increase productivity and software reliability as they provide high-level data representation and control structures. Modern languages are widely used in software development for mobile, desktop, and server devices, but their adoption is limited in real-time embedded systems. There is clear interest in supporting modern languages in embedded devices as emerging markets, like the Internet of Things, demand ever smarter and more reliable products. Multiple commercial and
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Betts, Alan K., Ahmed B. Tawfik, and Raymond L. Desjardins. "Revisiting Hydrometeorology Using Cloud and Climate Observations." Journal of Hydrometeorology 18, no. 4 (2017): 939–55. http://dx.doi.org/10.1175/jhm-d-16-0203.1.

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Abstract This paper uses 620 station years of hourly Canadian Prairie climate data to analyze the coupling of monthly near-surface climate with opaque cloud, a surrogate for radiation, and precipitation anomalies. While the cloud–climate coupling is strong, precipitation anomalies impact monthly climate for as long as 5 months. The April climate has memory of precipitation anomalies back to freeze-up in November, mostly stored in the snowpack. The summer climate has memory of precipitation anomalies back to the beginning of snowmelt in March. In the warm season, mean temperature is strongly co
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Betts, Alan, and Raymond Desjardins. "Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset." Environments 5, no. 12 (2018): 129. http://dx.doi.org/10.3390/environments5120129.

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Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, resp
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Shane, Hillary, and Kimberly Klonowski. "Thymic stromal lymphopoietin directly influences the respiratory CD8 T cell response to influenza virus infection. (P3230)." Journal of Immunology 190, no. 1_Supplement (2013): 124.26. http://dx.doi.org/10.4049/jimmunol.190.supp.124.26.

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Abstract Mucosal surfaces are tightly regulated immune microenvironments with unique anatomical structures, profiles of cells and cytokines. These aspects, coupled with infection derived inflammatory signals, can differentially modulate downstream immunological responses. Thymic stromal lymphopoietin (TSLP) is a cytokine expressed in mucosal surfaces where its role in CD4 T cell immunity has been well studied. However, whether TSLP directly affects anti-viral mucosal CD8 T cell responses is less understood. Here, we show that influenza induces TSLP production in a lung epithelial cell line and
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Dissertations / Theses on the topic "Data tightly coupled memory"

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Steffan, J. Gregory. "The potential for thread-level data speculation in tightly coupled multiprocessors." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ28852.pdf.

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Cesarini, Daniele. "OpenMP task scheduling strategies to mitigate hardware variability in tightly-coupled shared memory clusters." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7759/.

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In questa tesi sono stati apportati due importanti contributi nel campo degli acceleratori embedded many-core. Abbiamo implementato un runtime OpenMP ottimizzato per la gestione del tasking model per sistemi a processori strettamente accoppiati in cluster e poi interconnessi attraverso una network on chip. Ci siamo focalizzati sulla loro scalabilità e sul supporto di task di granularità fine, come è tipico nelle applicazioni embedded. Il secondo contributo di questa tesi è stata proporre una estensione del runtime di OpenMP che cerca di prevedere la manifestazione di errori dati da fenomeni di
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Lemon, Alexander Michael. "A Shared-Memory Coupled Architecture to Leverage Big Data Frameworks in Prototyping and In-Situ Analytics for Data Intensive Scientific Workflows." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7545.

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There is a pressing need for creative new data analysis methods whichcan sift through scientific simulation data and produce meaningfulresults. The types of analyses and the amount of data handled by currentmethods are still quite restricted, and new methods could providescientists with a large productivity boost. New methods could be simpleto develop in big data processing systems such as Apache Spark, which isdesigned to process many input files in parallel while treating themlogically as one large dataset. This distributed model, combined withthe large number of analysis libraries created f
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Lin, Jung-Tsan, and 林榮燦. "Optimizing Linux Kernel Performance with Tightly-Coupled Memory." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/42391073193637712839.

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碩士<br>國立交通大學<br>電機學院電機與控制學程<br>100<br>TCM (Tightly-Coupled Memory) is advantaged of high-speed data access with lower power consumption than the traditional memory architecture. As such, TCM is a best fit to hold mission critical routines and data structures. Prior research of this area has emphasized on how TCM can be applied to non-OS embedded applications, like media streaming, to improve the overall performance. Possibly due to large and complicated Linux kernel code base and its data structures, there is no discussion regarding the performance impact when placing OS kernel functions into
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Tasi, Tzung-Bow, and 蔡宗保. "Efficient Compiling Programs for Tightly-Coupled Distributed Memory Computers." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/84288792497222336859.

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碩士<br>國立中正大學<br>電機工程研究所<br>81<br>It is widely accepted that distributed memory parallel computers will play an important role in solving computation- intensive problems. However, the design of an algorithm in a distributed memory system is time-consuming and error-prone, because a programmer is forced to manage both parallelism and communication. In this thesis, we present a systematic method for compiling programs on distributed memory parallel computers. We will study the storage manageme
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Zhao, Tao. "Identification of Online Users' Social Status via Mining User-Generated Data." Doctoral thesis, 2019. http://hdl.handle.net/21.11130/00-1735-0000-0003-C1B1-A.

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Books on the topic "Data tightly coupled memory"

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Steffan, J. Gregory. The potential for thread-level data speculation in tightly coupled multiprocessors. National Library of Canada = Bibliothèque nationale du Canada, 1999.

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Keating, Michael. Simple art of SoC design: Closing the gap between RTL and ESL. Springer, 2011.

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Yang, Ada. PIC32CZ CA70_GC70 Tightly Coupled Memory (TCM). Microchip Technology Incorporated, 2018.

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Aiyappa, Rekha. PIC32CZ CA70_GC70 Tightly Coupled Memory (TCM. Microchip Technology Incorporated, 2017.

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Takenaka, Norio. AN2499 - PIC32CZ CA70/GC70 Tightly Coupled Memory (TCM) (KC). Microchip Technology Incorporated, 2018.

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Erdem, Uğur Murat, Nicholas Roy, John J. Leonard, and Michael E. Hasselmo. Spatial and episodic memory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0029.

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The neuroscience of spatial memory is one of the most promising areas for developing biomimetic solutions to complex engineering challenges. Grid cells are neurons recorded in the medial entorhinal cortex that fire when rats are in an array of locations in the environment falling on the vertices of tightly packed equilateral triangles. Grid cells suggest an exciting new approach for enhancing robot simultaneous localization and mapping (SLAM) in changing environments and could provide a common map for situational awareness between human and robotic teammates. Current models of grid cells are w
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Book chapters on the topic "Data tightly coupled memory"

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Wittig, Robert, Friedrich Pauls, Emil Matus, and Gerhard Fettweis. "Access Interval Prediction for Tightly Coupled Memory Systems." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27562-4_16.

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Khouri, Selma, and Ladjel Bellatreche. "Traceability of Tightly Coupled Phases of Semantic Data Warehouse Design." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26148-5_33.

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Nestorov, Svetlozar, and Shalom Tsur⋆. "Integrating Data Mining with Relational DBMS: A Tightly-Coupled Approach." In Next Generation Information Technologies and Systems. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48521-x_23.

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Razilov, Viktor, Robert Wittig, Emil Matúš, and Gerhard Fettweis. "Tagged Geometric History Length Access Interval Prediction for Tightly Coupled Memory Systems." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15074-6_6.

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Yeomans, Martin R. "Umami and Satiety." In Food and Health. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32692-9_5.

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AbstractThis chapter evaluates evidence from human studies that umami taste may enhance satiety. The author elaborates on the idea that humans evolved umami taste to detect and regulate protein intake, providing wider evidence that protein intake is more tightly regulated than other macronutrients and discussing specific evidence that protein is the most satiating. Three strands of evidence that suggest umami may have a role in satiety are evaluated. (1) Evidence from key studies that tested acute effects of manipulated umami taste on satiety in adult volunteers suggests that umami may enhance satiety, especially when coupled with protein intake. (2) A review of studies exploring the role of umami in infant feeding suggests that augmenting umami taste in bottle-fed babies leads to slower growth, implying that the presence of umami taste leads to greater satiety. (3) Evidence from studies exploring responses to umami in relation to protein deprivation suggests that sensitivity to umami varies depending on both acute and habitual protein need state, consistent with a regulatory role for umami involving satiety. This chapter draws these strands of evidence together to suggest two possible models of umami-induced satiety while noting limitations in the data that warrant further investigation.
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Ye, Xiaoyu, Dong Wang, Chenlu Yu, Zhuo Yang, and Along Zhang. "Deep Learning-Based Multi-Model Coupled Flood Season Daily Runoff Prediction Model." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-9184-2_10.

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AbstractAccurate runoff forecasting is of great significance for flood control, drought prevention, reservoir scheduling, and ecological protection. To explore the applicability of deep learning networks combined with signal processing techniques in runoff forecasting, an ICEEMDAN-VMD-CNN-LSTM daily runoff forecasting model for the flood season was developed. First, the original runoff series was decomposed using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Then, the complex series was further decomposed using Variational Mode Decomposition (VMD) to reduce data complexity. Next, each mode component was input into a Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM) combined model to extract local features of the data and capture long-term dependencies of the time series. Finally, the predicted values were reconstructed to obtain the final prediction results. Using the measured daily runoff data from the Hekou station in the Diaojiang basin as an example, the results showed that the ICEEMDAN-VMD-CNN-LSTM achieved testing MAE and NSE of 5.232 m3/s and 0.977, respectively, demonstrating excellent forecasting accuracy.
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Hou, Fangli, Jun Ma, Jack C. P. Cheng, and Helen H. L. Kwok. "Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.93.

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Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios
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Hou, Fangli, Jun Ma, Jack C. P. Cheng, and Helen H. L. Kwok. "Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.93.

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Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios
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Jones, Roger D., and Alan M. Jones. "A Proposed Mechanism for in vivo Programming Transmembrane Receptors." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57430-6_11.

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AbstractTransmembrane G-protein coupled receptors (GPCRs) are ideal drug targets because they resemble, in function, molecular microprocessors for which outcomes (e.g. disease pathways) can be controlled by inputs (extracellular ligands). The inputs here are ligands in the extracellular fluid and possibly chemical signals from other sources in the cellular environment that modify the states of molecular switches, such as phosphorylation sites, on the intracellular domains of the receptor. Like in an engineered microprocessor, these inputs control the configuration of output switch states that control the generation of downstream responses to the inputs.Many diseases with heterogeneous prognoses including, for example, cancer and diabetic kidney disease, require precise individualized treatment. The success of precision medicine to treat and cure disease is through its ability to alter the microprocessor outputs in a manner to improve disease outcomes. We previously established ab initio a model based on maximal information transmission and rate of entropy production that agrees with experimental data on GPCR performance and provides insight into the GPCR process. We use this model to suggest new and possibly more precise ways to target GPCRs with potential new drugs.We find, within the context of the model, that responses downstream of the GPCRs can be controlled, in part, by drug ligand concentration, not just whether the ligand is bound to the receptor. Specifically, the GPCRs encode the maximum ligand concentration the GPCR experiences in the number of active phosphorylation or other switch sites on the intracellular domains of the GPCR. This process generates a memory in the GPCR of the maximum ligand concentration seen by the GPCR. Each configuration of switch sites can generate a distinct downstream response bias. This implies that cellular response to a ligand may be programmable by controlling drug concentration. The model addresses the observation paradox that the amount of information appearing in the intracellular region is greater than amount of information stored in whether the ligand binds to the receptor. This study suggests that at least some of the missing information can be generated by the ligand concentration. We show the model is consistent with assay and information-flow experiments.In contrast to the current view of switch behavior in GPCR signaling, we find that switches exist in three distinct states: inactive (neither off nor on), actively on, or actively off. Unlike the inactive state, the active state supports a chemical flux of receptor configurations through the switch, even when the switch state is actively off. Switches are activated one at a time as ligand concentration reaches threshold values and does not reset because the ligand concentration drops below the thresholds. These results have clinical relevance. Treatment with drugs that target GPCR-mediated pathways can have increased precision for outputs by controlling switch configurations. The model suggests that, to see the full response spectrum, fully native receptors should be used in assay experiments rather than chimera receptors.Inactive states allow the possibility for novel adaptations. This expands the search space for natural selection beyond the space determined by pre-specified active switches.
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Zarins, Justs, and Michèle Weiland. "Progressive Load Balancing in Distributed Memory." In Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200033.

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System performance variability is a significant challenge to scalability of tightly-coupled iterative applications. Asynchronous variants perform better, but an imbalance in progress can result in slower convergence or even failure to converge, as old data is used for updates. In shared memory, this can be countered using progressive load balancing (PLB). We present a distributed memory extension to PLB (DPLB) by running PLB on nodes and adding a balancing layer between nodes. We demonstrate that this method is able to mitigate system performance variation by reducing global progress imbalance 1.08x–4.05x and time to solution variability 1.11x–2.89x. In addition, the method scales without significant overhead to 100 nodes.
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Conference papers on the topic "Data tightly coupled memory"

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Guo, Yuanyuan, Xianqiang Zhu, Qianzhen Zhang, and Bin Liu. "A Time-tightly Coupled UAV Control Scheduling Scheme." In 2024 IEEE 9th International Conference on Data Science in Cyberspace (DSC). IEEE, 2024. https://doi.org/10.1109/dsc63484.2024.00068.

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Chengyan, He, Wu Luoyu, Zhaolin Zhang, Wang Ling, and Tao Mingliang. "Spoofing Detection Algorithm for Tightly Coupled INS/GNSS Integration Model with robust Averaging Measurement." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10868403.

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Zou, Biao, Weida Ren, Xiaokang Zhu, Yubo Gao, and Songtao Zhu. "A Multi-Source Fusion Lightweight Tightly Coupled LiDAR-Inertial-GNSS SLAM for Unmanned Aerial Vehicle Detection." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824651.

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Verma, Praveen, Anuj Dhillon, Ashfaque Ahmed, et al. "CMOSP18 FD-SOI Technology Based MCU Achieving High Performance of 1.2GHz Using High Speed, Optimized Leakage & High Density Tightly Coupled Memory (TCM)." In 2025 38th International Conference on VLSI Design and 2025 24th International Conference on Embedded Systems (VLSID). IEEE, 2025. https://doi.org/10.1109/vlsid64188.2025.00074.

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Wang, Ning, Yubo Gao, Xiaokang Zhu, Weida Ren, Biao Zou, and Teng Fang. "LIRO-SLAM: A Tightly-Coupled LiDAR-Inertial Slam System for Robust UAV Navigation and Mapping in UHV Substations and Converter Stations." In 2024 3rd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2024. https://doi.org/10.1109/cbase64041.2024.10824643.

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Nam, Jongwon, Youmin Kim, Jinhyeon Han, et al. "Analysis of 3row Failure Caused by Vulnerable Data Retention Failure Adjacent to Disconnected BCAT." In ISTFA 2024. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.istfa2024p0157.

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Abstract As dynamic random access memory (DRAM) chips grow in density and complexity, tightly packed word lines become increasingly susceptible to interference, potentially causing data retention failures. This study investigates a novel failure mechanism where disconnected buried channel array transistors (BCATs) create interference affecting three adjacent word lines (3row failure). Through systematic analysis of voltage, temperature, and operational sequences, we demonstrate that the pass gate effect significantly impairs dynamic data retention, leading to these 3row failures. Our findings
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Surrey, Stefan, Biel Ortun, Felix Wienke, and Khiem-Van Truong. "Investigation Of The Structural Blade Dynamics And Aeroelastic Behavior Of The 7A Rotor." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11431.

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Deficits in rotor load prediction are often caused by inadequate modeling of structural blade dynamics. The elastic blade behavior needs to be validated as accurately as possible to predict rotor loads precisely. The structural blade dynamics of 1-D and 3-D finite element models in a multibody environment are investigated in terms of frequencies and aeroelastic behavior. Eigenfrequencies of the 1st torsion mode and the 4th flap mode of the reduced 3-D blade model exhibit a stronger coupling than the 1-D analysis prediction. The tightly coupled simulation with the advanced 1-D beam chain modeli
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Schäferlein, Ulrich, Manuel Keßler, and Ewald Krämer. "Cheeseman Award Paper: Aeroelastic Simulation of the Tail Shake Phenomenon." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12836.

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Rotor-fuselage interactions continue to pose a challenge during the design phase of a new helicopter. Quite often, the prototype phase is faced with problems in fast-forward flight caused by strong interactions at the tail, the so-called tail shake phenomenon. The wake of the main rotor, the rotor hub and the airframe impinges on the tail boom causing an excitation of low-frequency eigenmodes of the entire helicopter airframe. The resulting vibrations reduce flight comfort and flight stability in some critical cases. Previous approaches to solve the problem were restricted to wind tunnel tests
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Burgio, Paolo, Giuseppe Tagliavini, Andrea Marongiu, and Luca Benini. "Enabling Fine-Grained OpenMP Tasking on Tightly-Coupled Shared Memory Clusters." In Design Automation and Test in Europe. IEEE Conference Publications, 2013. http://dx.doi.org/10.7873/date.2013.306.

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Burgio, Paolo, Giuseppe Tagliavini, Francesco Conti, Andrea Marongiu, and Luca Benini. "Tightly-coupled hardware support to dynamic parallelism acceleration in embedded shared memory clusters." In Design Automation and Test in Europe. IEEE Conference Publications, 2014. http://dx.doi.org/10.7873/date.2014.169.

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