RTRLib: A High-Level Modeling Tool for the Implementation of Dynamically Partial Reconfigurable System-on-Chips

Ivo, Regina Marcela; Muñoz, Daniel M.

doi:10.1109/reconfig48160.2019.8994779

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…The static modules and the dynamic modules, i.e., the Linear, RBFNN, and MLPNN model predictors, were introduced into the Run-Time Reconfiguration Library (RTRLib) tool [24] to obtain the necessary scripts that automatically obtain the physical implementation of the overall DPR solution.…”

Section: Physical Implementation Of the Dpr-solutionmentioning

confidence: 99%

“…Thus, the tool translates 308 the content of the RMs and the static logic into its HDL 309 description. For more details about the RTRLib, the reader 310 is referred to [24].…”

Section: Physical Implementation Of the Dpr-solutionmentioning

confidence: 99%

“…The recurrent issue designers must deal with when designing DPR systems is the difficulty to assert its behavioral characteristics in the application domain, such as the reconfiguration time [21], performance [22], power efficiency [23], and its impact on the device as a whole. This work makes use of a high-level modeling tool for DPR System of Chips (SoCs), named RTRLib [24], allowing non-expert users to develop run-time reconfigurable applications.…”

mentioning

confidence: 99%

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Dynamic Reconfiguration for Multi-Magnet Tracking in Myokinetic Prosthetic Interfaces

Pertuz,

Mendes,

Gherardini

et al. 2024

Preprint

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Recently myokinetic interfaces have been proposed to exploit magnet tracking for controlling bionic prostheses. This interface derives information about muscle contractions from permanent magnets implanted into the amputee’s forearm muscles. Machine learning models have been mapped on Field Programmable Gate Arrays (FPGAs) to track a single magnet, achieving good precision and computational efficiency, but consuming a large area and hardware resources. To track several magnets, here we propose a novel solution based on dynamic partial reconfiguration, switching three prediction models: a linear regressor, a radial basis function neural network, and a multi-layer perceptron neural network. A system with five magnets and 128 magnetic sensor inputs was used and experimental data were collected to train a system with five hardware predictors. To reduce the complexity of the models, we applied principal component analysis, ranking by correlation the number of inputs of each model. This run-time reconfigurable solution allows the circuits to be reconfigured in order to select the most reliable predictor model for each magnet while the rest of the circuit continues to operate extracting the most significant information from the captured signals. Thus, the proposed solution remarkably reduces the hardware occupation and improves the computational efficiency compared to previous solutions.

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Section: Physical Implementation Of the Dpr-solutionmentioning

confidence: 99%

“…Thus, the tool translates 308 the content of the RMs and the static logic into its HDL 309 description. For more details about the RTRLib, the reader 310 is referred to [24].…”

Section: Physical Implementation Of the Dpr-solutionmentioning

confidence: 99%