Invention Grant
- Patent Title: Fixed-weighting-code learning device
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Application No.: US16493889Application Date: 2018-02-13
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Publication No.: US11625593B2Publication Date: 2023-04-11
- Inventor: Tetsuya Asai
- Applicant: NATIONAL UNIVERSITY CORPORATION HOKKAIDO UNIVERSITY
- Applicant Address: JP Sapporo
- Assignee: NATIONAL UNIVERSITY CORPORATION HOKKAIDO UNIVERSITY
- Current Assignee: NATIONAL UNIVERSITY CORPORATION HOKKAIDO UNIVERSITY
- Current Assignee Address: JP Sapporo
- Agency: Sughrue Mion, PLLC
- Priority: JPJP2017-048421 20170314
- International Application: PCT/JP2018/004786 WO 20180213
- International Announcement: WO2018/168293 WO 20180920
- Main IPC: G06N3/063
- IPC: G06N3/063 ; G06N3/04 ; G06N3/08 ; G06N3/088

Abstract:
A neural network circuit is provided with which it is possible to significantly reduce the area occupied by the connection unit of a full connection (FC)-type neural network circuit. An analog-type neural network circuit constitute a learning apparatus having a self-learning function and corresponding to a brain function, wherein the neural network comprises: a plurality (n) of input-side neurons; a plurality (m, and including cases when n=m) of output-side neurons; (n×m) connection units each connecting one input-side neuron and one output-side neuron; and a self-learning control unit, the (n×m) connection units being constituted from connection units corresponding to only the positive weighting function as a brain function, and connection units corresponding to only the negative weighting function as the brain function.
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