Document Detail

MedLine Citation:
PMID:  21243729     Owner:  NLM     Status:  In-Data-Review    
The Graphics Processing Unit (GPU) originally designed for rendering graphics and which is difficult to program for other tasks, has since evolved into a device suitable for general-purpose computations. As a result graphics hardware has become progressively more attractive yielding unprecedented performance at a relatively low cost. Thus, it is the ideal candidate to accelerate a wide variety of data parallel tasks in many fields such as in Machine Learning (ML). As problems become more and more demanding, parallel implementations of learning algorithms are crucial for a useful application. In particular, the implementation of Neural Networks (NNs) in GPUs can significantly reduce the long training times during the learning process. In this paper we present a GPU parallel implementation of the Back-Propagation (BP) and Multiple Back-Propagation (MBP) algorithms, and describe the GPU kernels needed for this task. The results obtained on well-known benchmarks show faster training times and improved performances as compared to the implementation in traditional hardware, due to maximized floating-point throughput and memory bandwidth. Moreover, a preliminary GPU based Autonomous Training System (ATS) is developed which aims at automatically finding high-quality NNs-based solutions for a given problem.
Noel Lopes; Bernardete Ribeiro
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  International journal of neural systems     Volume:  21     ISSN:  0129-0657     ISO Abbreviation:  Int J Neural Syst     Publication Date:  2011 Feb 
Date Detail:
Created Date:  2011-01-18     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9100527     Medline TA:  Int J Neural Syst     Country:  Singapore    
Other Details:
Languages:  eng     Pagination:  31-47     Citation Subset:  IM    
CISUC, Department of Informatics Engineering, University of Coimbra, Portugal.
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