Journal ArticleDOI
Economic LSTM Approach for Recurrent Neural Networks
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TLDR
A new approach to Long Short-Term Memory (LSTM) that aims to reduce the cost of the computation unit and has fewer units compared to the existing LSTM versions which makes it very attractive in processing speed and hardware design cost.Abstract:
Recurrent Neural Networks (RNNs) have become a popular method for learning sequences of data. It is sometimes tough to parallelize all RNN computations on conventional hardware due to its recurrent nature. One challenge of RNN is to find its optimal structure for RNN because of computing complex hidden units that exist. This brief presents a new approach to Long Short-Term Memory (LSTM) that aims to reduce the cost of the computation unit. The proposed Economic LSTM (ELSTM) is designed using a few hardware units to perform its functionality. ELSTM has fewer units compared to the existing LSTM versions which makes it very attractive in processing speed and hardware design cost. The proposed approach is tested using three datasets and compared with other methods. The simulation results show the proposed method has comparable accuracy with other methods. At the hardware level, the proposed method is implemented on Altera FPGA.read more
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References
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Long short-term memory
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Gradient-based learning applied to document recognition
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
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Deep learning in neural networks
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