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

Author
Jaksic, Z.; Cadenelli, N.; Buchaca, D.; Polo, J.; Berral, J.; Carrera, D.
Type of activity
Journal article
Journal
Future generation computer systems
Date of publication
2020-03-01
Volume
104
Number
March 2020
First page
201
Last page
211
DOI
10.1016/j.future.2019.10.025
Project funding
Computación de Altas Prestaciones VII
HiEST: Holistic Integration of Emerging Supercomputing Technologies
Models de Programacio i Entorns d'eXecució PARal.lels
Repository
http://hdl.handle.net/2117/186484 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0167739X19313676 Open in new window
Abstract
Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for modeling multidimensional time series such as human motion, workload characterization, city traffic analysis. The process of learning and inf...
Citation
Jaksic, Z. [et al.]. A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL. "Future generation computer systems", 1 Març 2020, vol. 104, núm. March 2020, p. 201-211.
Keywords
ANN, CRBM, FPGA, GEMM, OpenCL, Time-series
Group of research
CAP - High Performace Computing Group

Participants

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