Relevant bibliographies by topics / Data tightly coupled memory

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Data tightly coupled memory'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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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 code optimization, memory management, and implementing interrupt-driven data transfer. Application code can be optimized by getting rid of redundant code, duplicate functions and function inlining, function cloning which reduces the size of the code with increase in reusability. By moving the data, variables to data tightly coupled memory (DTCM) and instructions, functions to instruction tightly coupled memory (ITCM), the speed of the CPU increases which reduces the load on CPU. The conventional polling method which increases the CPU load can be reduced by implementing the same in interrupt-driven data transfer. The load on the CPU has reduced from 89.53% to 29.58%.
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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 code optimization, memory management, and implementing interrupt-driven data transfer. Application code can be optimized by getting rid of redundant code, duplicate functions and function inlining, function cloning which reduces the size of the code with increase in reusability. By moving the data, variables to data tightly coupled memory (DTCM) and instructions, functions to instruction tightly coupled memory (ITCM), the speed of the CPU increases which reduces the load on CPU. The conventional polling method which increases the CPU load can be reduced by implementing the same in interrupt-driven data transfer. The load on the CPU has reduced from 89.53% to 29.58%.
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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 they depend on the problem size—both the programs and configuration data must be generated at runtime. However, pure just-in-time compilation is impractical, because mapping a loop program onto a TCPA entails solving multiple NP-complete problems. As a solution, this article proposes a unique mixed static/dynamic approach called symbolic loop compilation. It is shown that at compile time, the NP-complete problems (modulo scheduling, register allocation, and routing) can still be solved to optimality in a symbolic way resulting in a so-called symbolic configuration , a space-efficient intermediate representation parameterized in the loop bounds and number of PEs. This phase is called symbolic mapping . At runtime, for each requested accelerated execution of a loop program with given loop bounds and known number of available PEs, a concrete configuration , including PE programs and configuration data for all other components, is generated from the symbolic configuration according to these parameter values. This phase is called instantiation . We describe both phases in detail and show that instantiation runs in polynomial time with its most complex step, program instantiation, not directly depending on the number of PEs and thus scaling to arbitrary sizes of TCPAs. To validate the efficiency of this mixed static/dynamic compilation approach, we apply symbolic loop compilation to a set of real-world loop programs from several domains, measuring both compilation time and space requirements. Our experiments confirm that a symbolic configuration is a space-efficient representation suited for systems with little memory—in many cases, a symbolic configuration is smaller than even a single concrete configuration instantiated from it—and that the times for the runtime phase of program instantiation and configuration loading are negligible and moreover independent of the size of the available processor array. To give an example, instantiating a configuration for a matrix-matrix multiplication benchmark takes equally long for 4× 4 and 32× 32 PEs.
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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 transfers. The performance evaluation of a system integrating C-SRAM within the DMU compared to a reference scalar system architecture shows an increase from ×5.73 to ×11.01 in speed-up and from ×29.49 to ×46.67 in energy reduction, versus a system integrating C-SRAM without any transfer mechanism compared to a reference scalar system architecture.
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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-hydrodynamics code, an Eulerian higher-order hydrodynamics turbulence modeling code, and an Eulerian coupled laser-plasma interaction code. We compress relevant quantities after each time-step to approximate the effects of tightly coupled compression and study the compression rates to estimate memory and disk-bandwidth reduction. We find that the error characteristics of compression algorithms must be carefully considered in the context of the underlying physics being modeled.
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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 specification. The three evaluated architectures are the Tightly Coupled Streaming, Protocol Adapter FIFO, and Direct Memory Access (DMA) Streaming architectures. When comparing the tightly coupled architecture with the one including the DMA, the experiments in this paper show an almost 3× decrease in frame latency when using the DMA. Nevertheless, this comes at the price of an increase in FPGA resource utilization as follows: LUT (2.5×), LUTRAM (3×), FF (3.4×), and BRAM (1.2×). Four different test scenarios were run for the DMA architecture, showcasing the best and worst practices for data organization. The evaluation results highlight that poor data organization can lead to a more than 7× increase in latency. The CPU model was selected as the newly released MicroBlaze-V softcore processor. The designs presented herein successfully operate on a popular low-cost Field-Programmable Gate Array (FPGA) development board at 100 MHz. Block diagrams, FPGA resource utilization, and latency metrics are presented. Finally, based on the evaluation results, possible improvements are discussed.
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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 open-source projects, such as Zerynth and MicroPython, are attempting to provide support. But these projects rely on software garbage collectors that impose high overheads and introduce unpredictable pauses, preventing their use in many embedded applications. These limitations arise from the unsuitability of conventional processors for performing efficient, predictable garbage collection. We propose the Integrated Hardware Garbage Collector (IHGC); a garbage collector tightly coupled with the processor that runs continuously in the background. Further, we introduce a static analysis technique to guarantee that real-time programs are never paused by the collector. Our design allocates a memory cycle to the collector when the processor is not using the memory. The IHGC achieves this by careful division of collection work into single-memory-access steps that are interleaved with the processor’s memory accesses. As a result, our collector eliminates run-time overheads and enables real-time program analysis. The principles behind the IHGC can be used in conjunction with existing architectures. For example, we simulated the IHGC alongside the ARMv6-M architecture. Compared to a conventional processor, our experiments indicate that the IHGC offers 1.5–7 times better performance for programs that rely on garbage collection. The IHGC delivers the benefits of garbage-collected languages with real-time performance but without the complexity and overheads inherent in software collectors.
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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 correlated to opaque cloud anomalies, but only weakly to precipitation anomalies. Mixing ratio anomalies are correlated to precipitation, but only weakly to cloud. The diurnal cycle of mixing ratio shifts upward with increasing precipitation anomalies. Positive precipitation anomalies are coupled to a lower afternoon lifting condensation level and a higher afternoon equivalent potential temperature; both favor increased convection and precipitation. Regression coefficients on precipitation increase from wet to dry conditions. This is consistent with increased uptake of soil water when monthly precipitation is low, until drought conditions are reached, and also consistent with gravity satellite observations. Regression analysis shows monthly opaque cloud cover is tightly correlated to three climate variables that are routinely observed: diurnal temperature range, mean temperature, and mean relative humidity. The set of correlation coefficients, derived from cloud and climate observations, could be used to evaluate the representation of the land–cloud–atmosphere system in both forecast and climate models.
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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, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.
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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 infected mice. To test the direct role of TSLP on mucosal CD8 T cell responses we used a competitive adoptive transfer system where equal numbers of OVA-specific OT-I and TSLP receptor deficient OT-I cells are co-transferred into recipient mice infected with influenza expressing OVA. We found that TSLPR-/- OT-I CD8 T cells proliferate less early post infection (pi) and have decreased accumulation in the respiratory tract both at days 8 and 50pi. Interestingly, the lack of TSLP signaling imparted a higher level of CD62L expression on TSLPR-/- OT-I memory cells. Together these data suggest that TSLP signaling may directly drive the expansion and development of CD8 effector phenotypes while concomitantly limiting long term responses. Thus, TSLP participates in the regulation of immune responses in the respiratory tract and modulation of TSLP levels may promote long term immunity at mucosal sites.
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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 variability tramite una schedulazione efficiente del carico di lavoro.
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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 for the platform, makesSpark ideal for processing simulation output.Unfortunately, the filesystem becomes a major bottleneck in any workflowthat uses Spark in such a fashion. Faster transports are notintrinsically supported by Spark, and its interface almost denies thepossibility of maintainable third-party extensions. By leveraging thesemantics of Scala and Spark's recent scheduler upgrades, we forceco-location of Spark executors with simulation processes and enable fastlocal inter-process communication through shared memory. This provides apath for bulk data transfer into the Java Virtual Machine, removing thecurrent Spark ingestion bottleneck.Besides showing that our system makes this transfer feasible, we alsodemonstrate a proof-of-concept system integrating traditional HPC codeswith bleeding-edge analytics libraries. This provides scientists withguidance on how to apply our libraries to gain a new and powerful toolfor developing new analysis techniques in large scientific simulationpipelines.
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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 TCM. An improper arrangement of kernel functions in TCM can contrarily downgrade the performance. In this paper, we analyze the utilization and performance impact of TCM, and classify Linux kernel functions into groups per the TCM capacity. Then, we select different groups of Linux kernel functions at the compiling time and place these function groups into TCM without swapping them out during execution. By conducting the experiments with lmbench, we find that placing exec() or schedule() into TCM can reduce the local communication latency by a factor of 13% - 14%.
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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 management of data arrays and the execution schedule arrangement of Do-loop programs on distributed memory parallel computers. First, we derive a dynamic programming algorithm for data distribution. Second, we improve the communication time by pipelining data. Third, we use data-dependence information for pipelining data. Jacobi's iterative algorithm, a successive over-relaxation iterative algorithm, and the Gauss elimination algorithm for linear systems are used to illustrate our method.
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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 well suited to robotics, as they utilize input from self-motion and sensory flow similar to inertial sensors and visual odometry in robots. Computational models, supported by in vivo neural activity data, demonstrate how grid cell representations could provide a substrate for goal-directed behavior using hierarchical forward planning that finds novel shortcut trajectories in changing environments.
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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 reveal a previously unidentified defect mechanism in advanced DRAM technology and emphasize the importance of comprehensive testing protocols for detecting and characterizing emerging failure modes. This work contributes to the broader effort of improving DRAM reliability in modern computing systems.
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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 modeling approach is used to investigate the aeroelastic behavior of the 7A rotor in high-speed forward flight. The potential of advanced rotor blade modeling in the multibody software SIMPACK in terms of rotor loads prediction is evaluated. In comparison to experimental data and the loose-coupling environment of ONERA (HOST-elsA), aerodynamical and structural loads are in good agreement. The characteristic 5/rev elastic torsion behavior of the 7A rotor blade has been captured leading to an improvement of the lift force prediction. The coupled SIMPACK-TAU predictions with an advanced 1-D beam model and the HOST-elsA results show a significant improvement over previous computations.
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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 due to the lack of predictability in terms of numerical methods. A high-fidelity simulation presented in this study by means of a time-resolved coupling between the flow and structural behavior of the helicopter shows significant progress in the prediction potential of the investigated phenomenon. Utilizing higher order methods for the CFD simulation, tightly coupled with a modal-based CSD simulation of the airframe, very good agreement with flight test data of the relevant low-frequency eigenmodes could be achieved.
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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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