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238 results on '"Morie, T."'

3. An efficient hardware-oriented dropout algorithm

Author

Yeoh, Y.J., Morie, T., Tamukoh, H., Yeoh, Y.J., Morie, T., and Tamukoh, H.

Abstract

This paper proposes a hardware-oriented dropout algorithm, which is efficient for field programmable gate array (FPGA) implementation. In deep neural networks (DNNs), overfitting occurs when networks are overtrained and adapt too well to training data. Consequently, they fail in predicting unseen data used as test data. Dropout is a common technique that is often applied in DNNs to overcome this problem. In general, implementing such training algorithms of DNNs in embedded systems is difficult due to power and memory constraints. Training DNNs is power-, time-, and memory- intensive; however, embedded systems require low power consumption and real-time processing. An FPGA is suitable for embedded systems for its parallel processing characteristic and low operating power; however, due to its limited memory and different architecture, it is difficult to apply general neural network algorithms. Therefore, we propose a hardware-oriented dropout algorithm that can effectively utilize the characteristics of an FPGA with less memory required. Software program verification demonstrates that the performance of the proposed method is identical to that of conventional dropout, and hardware synthesis demonstrates that it results in significant resource reduction.

Published
2020

4. Reconstructing bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems

Author

Hokkaido University of Science, The University of Tokyo, The University of Tokyo, Tokyo Denki University, Kyushu Institute of Technology, The University of Tokyo, The University of Tokyo, Itoh, Y., Uenohara, S., Adachi, M., Morie, T., Aihara, K., Hokkaido University of Science, The University of Tokyo, The University of Tokyo, Tokyo Denki University, Kyushu Institute of Technology, The University of Tokyo, The University of Tokyo, Itoh, Y., Uenohara, S., Adachi, M., Morie, T., and Aihara, K.

Abstract

type:Journal Article, Bifurcation-diagram reconstruction estimates various attractors of a system without observing all of them but only from observing several attractors with different parameter values. Therefore, the bifurcation-diagram reconstruction can be used to investigate how attractors change with the parameter values, especially for real-world engineering and physical systems for which only a limited number of attractors can be observed. Although bifurcation diagrams of various systems have been reconstructed from time-series data generated in numerical experiments, the systems that have been targeted for reconstructing bifurcation diagrams from time series measured from physical phenomena so far have only been continuous-time dynamical systems. In this paper, we reconstruct bifurcation diagrams only from time-series data generated by electronic circuits in discrete-time dynamical systems with different parameter values. The generated time-series datasets are perturbed by dynamical noise and contaminated by observational noise. To reconstruct the bifurcation diagrams only from the time-series datasets, we use an extreme learning machine as a time-series predictor because it has a good generalization property. Hereby, we expect that the bifurcation-diagram reconstruction with the extreme learning machine is robust against dynamical noise and observational noise. For quantitatively verifying the robustness, the Lyapunov exponents of the reconstructed bifurcation diagrams are compared with those of the bifurcation diagrams generated in numerical experiments and by the electronic circuits., source:https://doi.org/10.1063/1.5119187

Published
2020

7. Three-terminal ferroelectric synapse device with concurrent learning function for artificial neural networks.

Author

Nishitani, Y., Kaneko, Y., Ueda, M., Morie, T., and Fujii, E.

Subjects
FERROELECTRIC crystals, CONCURRENT engineering, ARTIFICIAL neural networks, FIELD-effect transistors, PIEZOELECTRIC materials
Abstract

Spike-timing-dependent synaptic plasticity (STDP) is demonstrated in a synapse device based on a ferroelectric-gate field-effect transistor (FeFET). STDP is a key of the learning functions observed in human brains, where the synaptic weight changes only depending on the spike timing of the pre- and post-neurons. The FeFET is composed of the stacked oxide materials with ZnO/Pr(Zr,Ti)O3 (PZT)/SrRuO3. In the FeFET, the channel conductance can be altered depending on the density of electrons induced by the polarization of PZT film, which can be controlled by applying the gate voltage in a non-volatile manner. Applying a pulse gate voltage enables the multi-valued modulation of the conductance, which is expected to be caused by a change in PZT polarization. This variation depends on the height and the duration of the pulse gate voltage. Utilizing these characteristics, symmetric and asymmetric STDP learning functions are successfully implemented in the FeFET-based synapse device by applying the non-linear pulse gate voltage generated from a set of two pulses in a sampling circuit, in which the two pulses correspond to the spikes from the pre- and post-neurons. The three-terminal structure of the synapse device enables the concurrent learning, in which the weight update can be performed without canceling signal transmission among neurons, while the neural networks using the previously reported two-terminal synapse devices need to stop signal transmission for learning. [ABSTRACT FROM AUTHOR]

Published
2012
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44. A fully integrated 0.13-μm CMOS mixed-signal SoC for DVD player applications

Author

Okamoto, K., primary, Morie, T., additional, Yamamoto, A., additional, Nagano, K., additional, Sushihara, K., additional, Nakahira, H., additional, Horibe, R., additional, Aida, K., additional, Takahashi, T., additional, Ochiai, M., additional, Soneda, A., additional, Kakiage, T., additional, Iwasaki, T., additional, Taniuchi, H., additional, Shibata, T., additional, Ochi, T., additional, Takiguchi, M., additional, Yamamoto, T., additional, Seike, T., additional, and Matsuzawa, A., additional

Published
2003
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