A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL

Jaks̆ić, Zoran; Cadenelli, Nicola; Buchaca, David; Polo, Jordà; Garcia, Josep Lluís Berral; Carrera, David

doi:10.1016/j.future.2019.10.025

Cited by 5 publications

(1 citation statement)

References 5 publications

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“…Statistical feature learning has gained substantial ground as an unsupervised learning method. Energy‐based unsupervised models became quite popular in time‐series forecasting applications [11–16].…”

Section: Introductionmentioning

confidence: 99%

Conditional restricted Boltzmann machine as a generative model for body‐worn sensor signals

Karakus

Köse

2020

IET signal process.

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Sensor‐based human activity classification requires time and frequency domain feature extraction techniques. The set of choice in time and frequency domain features may have a significant impact on the overall classification accuracy. Another problem is to train deep learning models with sufficient dataset. The use of generative models eliminates the requirement of choosing certain features of the signal. As a generative model, restricted Boltzmann machine (RBM) is an energy‐based probabilistic graphical model which factorises the probability distribution of a random variable over a binary probability distribution. Conditional restricted Boltzmann machines (CRBMs) is an extension to RBM, which can capture temporal information in time‐series signals and can be deployed as a generative model in classification. In this study, the authors show how CRBMs can be trained to learn signal features. They present four generative model training results, RBM, CRBM, generative adversarial network, Wasserstein generative adversarial network – gradient penalty and compare the models' performances with a performance criterion. They show that the CRBM model can generate signals closest to true signals with a significantly higher success rate as compared to other presented generative models. They present a statistical analysis of the findings and show that the findings significantly hold.

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Section: Introductionmentioning

confidence: 99%

Conditional restricted Boltzmann machine as a generative model for body‐worn sensor signals

Karakus

Köse

2020

IET signal process.

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New paradigm of FPGA-based computational intelligence from surveying the implementation of DNN accelerators

Chang

et al. 2022

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Unsharp masking image enhancement the parallel algorithm based on cross-platform

Song

Xiao

et al. 2022

Sci Rep

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In view of the low computational efficiency and the limitations of the platform of the unsharp masking image enhancement algorithm, an unsharp masking image enhancement parallel algorithm based on Open Computing Language (OpenCL) is proposed. Based on the analysis of the parallel characteristics of the algorithm, the problem of unsharp masking processing is implemented in parallel. Making use of the characteristics of data reuse in the algorithm, the effective allocation and optimization of global memory and constant memory are realized according to the access attributes of the data and the characteristics of the OpenCL storage model, and the use efficiency of off-chip memory is improved. Through the data storage access mode, the fast computing local memory access mode is discovered, and the logical data space transformation is used to convert the storage access mode, so as to improve the bandwidth utilization of the on-chip memory. The experimental results show that, compared with the CPU serial algorithm, the OpenCL accelerated unsharp masking image enhancement parallel algorithm greatly reduces the execution time of the algorithm while maintaining the same image quality, and achieves a maximum speedup of 16.71 times. The high performance and platform transplantation of the algorithm on different hardware platforms are realized. It provides a reference method for real-time processing of a large amount of data of high-resolution images for image enhancement.

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A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL

Cited by 5 publications

References 5 publications

Conditional restricted Boltzmann machine as a generative model for body‐worn sensor signals

Conditional restricted Boltzmann machine as a generative model for body‐worn sensor signals

New paradigm of FPGA-based computational intelligence from surveying the implementation of DNN accelerators

Unsharp masking image enhancement the parallel algorithm based on cross-platform

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