Hierarchical Ensemble Reduction and Learning for Resource-constrained Computing

Wang, Hongfei; Li, Jianwen; He, Kun

doi:10.1145/3365224

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…The translator proposed is not a classical one-pass or twopass translator [50]. It is a translator adapted to the generation of the NoC communication subsystem.…”

Section: A Generation Of a Low-level Model Of The Noc Communication S...mentioning

confidence: 99%

Electronic Computer-Aided Design for Low-Level Modeling of Networks-on-Chip

Lezhnev,

Zunin,

Amerikanov

et al. 2024

IEEE Access

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This article proposes a Network-on-Chip (NoC) communication subsystem model on the basis of which the Electronic Computer-Aided Design (ECAD) architecture in the form of software is implemented. It makes it possible to automate the process of preparing and generating an HDL description of the NoC model in the Verilog language. It is shown that not in all cases it is required to model the entire NoC. Often, it is necessary to model its certain parts, such as a communication subsystem, routing algorithm, and traffic control system. The developed model allows modeling a parameterized NoC communication subsystem to obtain an estimate of the consumed logical blocks and registers required for prototyping the communication subsystem. All components of the communication subsystem are implemented as separate modules due to which the hardware costs for adding the necessary components for the study are reduced because of the absence of the need to completely rework the model program code every time. The effectiveness of using the developed ECAD and low-level modeling automation methods to study the work of routing algorithms for NoC topologies is demonstrated.INDEX TERMS electronic computer-aided design (ECAD), HDL, network-on-chip (NoC), modeling, RTL.

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“…The translator proposed is not a classical one-pass or twopass translator [50]. It is a translator adapted to the generation of the NoC communication subsystem.…”

Section: A Generation Of a Low-level Model Of The Noc Communication S...mentioning

confidence: 99%

Electronic Computer-Aided Design for Low-Level Modeling of Networks-on-Chip

Lezhnev,

Zunin,

Amerikanov

et al. 2024

IEEE Access

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“…This is consistent with Mitchell et al (2002), who used EnKF with an ensemble size of 64 to assimilate radiosonde, satellite, and aircraft data into a dry, global, primitive-equation model. Over the past decade, the boost in computational power has opened the door to new approaches for improving simulation performance, including bagging ensemble filters, entropy ensemble filters, and hierarchical ensemble filters (Foroozand and Weijs, 2017;Wang et al, 2020).…”

Section: Sensitive Parameters Of the Enkf Assimilationmentioning

confidence: 99%

Improving Soil Salinity Simulation by Assimilating Electromagnetic Induction Data into HYDRUS Model Using Ensemble Kalman Filter

Yao¹,

Yang²,

Wang³

et al. 2021

J ENVIRON INFORM

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Assimilation of proximally and remotely sensed information on soil salinization-related attributes into a hydrological model is essential to improve the forecast performance of the profiled soil salinity dynamics for developing appropriate soil amendment practices. Although the family of ensemble Kalman filters (EnKF) is widely used in data assimilation, their applicability and reliability for soil salinization estimation requires further experimental validation. Here, we evaluated the assimilation performance of apparent electrical conductivity (ECa) data obtained from an electromagnetic induction meter (EM38) into the HYDRUS hydrological model. Results showed that the EnKF method improved the simulation accuracy of soil salinity at 0 ~ 100 cm soil depths, as indicated by the decreased root-mean-square error of 32.6 ~ 76.7% and increased Nash-Sutcliffe efficiency of 9.6 ~ 71.2%. The HYDRUS-simulated values with EnKF were closer to the measured values than the values simulated by the HYDRUS model, and this benefitted from updating the running trajectory of the HYDRUS model. The EnKF values derived from measured ECa data were better than HYDRUS-simulated values with EnKF. Soil salinity simulation was sensitive to ensemble size, error level, and ECa data depth. Considering the ensemble representativeness and computational efficiency, the optimal ensemble size was judged to be 50. The maximum acceptable observation error was 10%, and observation data to a depth of 100 cm was suggested in EnKF assimilation to minimize the root-mean-square error. It was concluded that proximally sensed EM38 data coupled with the EnKF algorithm is promising for improving the simulation performance and providing a prospective method for simulating large-scale ecological and hydrological processes by coupling multi-source data and hydrological models.

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Hierarchical Ensemble Reduction and Learning for Resource-constrained Computing

Cited by 2 publications

References 24 publications

Electronic Computer-Aided Design for Low-Level Modeling of Networks-on-Chip

Electronic Computer-Aided Design for Low-Level Modeling of Networks-on-Chip

Improving Soil Salinity Simulation by Assimilating Electromagnetic Induction Data into HYDRUS Model Using Ensemble Kalman Filter

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