13,172 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
Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework
Hardware accelerations of deep learning systems have been extensively
investigated in industry and academia. The aim of this paper is to achieve
ultra-high energy efficiency and performance for hardware implementations of
deep neural networks (DNNs). An algorithm-hardware co-optimization framework is
developed, which is applicable to different DNN types, sizes, and application
scenarios. The algorithm part adopts the general block-circulant matrices to
achieve a fine-grained tradeoff between accuracy and compression ratio. It
applies to both fully-connected and convolutional layers and contains a
mathematically rigorous proof of the effectiveness of the method. The proposed
algorithm reduces computational complexity per layer from O() to O() and storage complexity from O() to O(), both for training and
inference. The hardware part consists of highly efficient Field Programmable
Gate Array (FPGA)-based implementations using effective reconfiguration, batch
processing, deep pipelining, resource re-using, and hierarchical control.
Experimental results demonstrate that the proposed framework achieves at least
152X speedup and 71X energy efficiency gain compared with IBM TrueNorth
processor under the same test accuracy. It achieves at least 31X energy
efficiency gain compared with the reference FPGA-based work.Comment: 6 figures, AAAI Conference on Artificial Intelligence, 201
SurReal: enhancing Surgical simulation Realism using style transfer
Surgical simulation is an increasingly important element of surgical
education. Using simulation can be a means to address some of the significant
challenges in developing surgical skills with limited time and resources. The
photo-realistic fidelity of simulations is a key feature that can improve the
experience and transfer ratio of trainees. In this paper, we demonstrate how we
can enhance the visual fidelity of existing surgical simulation by performing
style transfer of multi-class labels from real surgical video onto synthetic
content. We demonstrate our approach on simulations of cataract surgery using
real data labels from an existing public dataset. Our results highlight the
feasibility of the approach and also the powerful possibility to extend this
technique to incorporate additional temporal constraints and to different
applications
Reverse Engineering Gene Networks with ANN: Variability in Network Inference Algorithms
Motivation :Reconstructing the topology of a gene regulatory network is one
of the key tasks in systems biology. Despite of the wide variety of proposed
methods, very little work has been dedicated to the assessment of their
stability properties. Here we present a methodical comparison of the
performance of a novel method (RegnANN) for gene network inference based on
multilayer perceptrons with three reference algorithms (ARACNE, CLR, KELLER),
focussing our analysis on the prediction variability induced by both the
network intrinsic structure and the available data.
Results: The extensive evaluation on both synthetic data and a selection of
gene modules of "Escherichia coli" indicates that all the algorithms suffer of
instability and variability issues with regards to the reconstruction of the
topology of the network. This instability makes objectively very hard the task
of establishing which method performs best. Nevertheless, RegnANN shows MCC
scores that compare very favorably with all the other inference methods tested.
Availability: The software for the RegnANN inference algorithm is distributed
under GPL3 and it is available at the corresponding author home page
(http://mpba.fbk.eu/grimaldi/regnann-supmat
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