Hardware Implementation of a DBM Network with Non-monotonic Neurons
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概要
- 論文の詳細を見る
In this paper, we report a study on hardware implementation of a Deterministic Boltzmann Machine (DBM) with non-monotonic neurons (non-monotonic DBM network). The hardware DBM network has fewer components than other neural networks. Results from numerical simulations show that the non-monotonic DBM network has high learning ability as compared to the monotonic DBM network. These results show that the non-monotonic DBM network has large potential for the implementation of a high functional neurochip. Then, we design and fabricate a neurochip of the non-monotonic DBM network of which measurement confirms that the high-functional large-scale neural system can be realized on a compact neurochip by using the non-monotonic neurons.
- 社団法人電子情報通信学会の論文
- 2002-03-01
著者
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Nakajima Kensuke
Research Institute Of Electrical Communication Tohoku University
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KINJO Mitsunaga
Laboratory for Electronic Intelligent Systems, Research Institute of Electrical Communication, Tohok
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SATO Shigeo
Laboratory for Electronic Intelligent Systems, Research Institute of Electrical Communication, Tohok
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Kinjo Mitsunaga
Department Of Electrical And Electronic Engineering Faculty Of Engineering University Of The Ryukyus
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Nakajima Koji
Laboratory For Brainware Reseach Institute Of Electrical Comunication Tohoku University:laboratory F
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Sato Shigeo
Laboratory For Brainware Systems Laboratory For Nanoelectronics And Spintronics Research Institute O
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Kinjo Mitsunaga
Laboratory for Brainware System, Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, Sendai 980-8577 Japan
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Sato Shigeo
Laboratory for Brainware System, Laboratory for Nanoelectronics and Spintronics, Research Institute of Electrical Communication, Tohoku University, Sendai 980-8577 Japan
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