74,984 research outputs found
On the Dynamics of a Recurrent Hopfield Network
In this research paper novel real/complex valued recurrent Hopfield Neural
Network (RHNN) is proposed. The method of synthesizing the energy landscape of
such a network and the experimental investigation of dynamics of Recurrent
Hopfield Network is discussed. Parallel modes of operation (other than fully
parallel mode) in layered RHNN is proposed. Also, certain potential
applications are proposed.Comment: 6 pages, 6 figures, 1 table, submitted to IJCNN-201
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a key technology for emerging
applications such as automatic speech recognition, machine translation or image
description. Long Short Term Memory (LSTM) networks are the most successful RNN
implementation, as they can learn long term dependencies to achieve high
accuracy. Unfortunately, the recurrent nature of LSTM networks significantly
constrains the amount of parallelism and, hence, multicore CPUs and many-core
GPUs exhibit poor efficiency for RNN inference. In this paper, we present
E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM
computation. The main goal of E-PUR is to support large recurrent neural
networks for low-power mobile devices. E-PUR provides an efficient hardware
implementation of LSTM networks that is flexible to support diverse
applications. One of its main novelties is a technique that we call Maximizing
Weight Locality (MWL), which improves the temporal locality of the memory
accesses for fetching the synaptic weights, reducing the memory requirements by
a large extent. Our experimental results show that E-PUR achieves real-time
performance for different LSTM networks, while reducing energy consumption by
orders of magnitude with respect to general-purpose processors and GPUs, and it
requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA
Tegra X1, E-PUR provides an average energy reduction of 92x
Recurrent Highway Networks
Many sequential processing tasks require complex nonlinear transition
functions from one step to the next. However, recurrent neural networks with
'deep' transition functions remain difficult to train, even when using Long
Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of
recurrent networks based on Gersgorin's circle theorem that illuminates several
modeling and optimization issues and improves our understanding of the LSTM
cell. Based on this analysis we propose Recurrent Highway Networks, which
extend the LSTM architecture to allow step-to-step transition depths larger
than one. Several language modeling experiments demonstrate that the proposed
architecture results in powerful and efficient models. On the Penn Treebank
corpus, solely increasing the transition depth from 1 to 10 improves word-level
perplexity from 90.6 to 65.4 using the same number of parameters. On the larger
Wikipedia datasets for character prediction (text8 and enwik8), RHNs outperform
all previous results and achieve an entropy of 1.27 bits per character.Comment: 12 pages, 6 figures, 3 table
Hierarchical Temporal Representation in Linear Reservoir Computing
Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the state signals. The potentiality of our approach is assessed on
the class of Multiple Superimposed Oscillator tasks. Furthermore, our
investigation provides useful insights to open a discussion on the main aspects
that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian
Workshop on Neural Networks, WIRN 201
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