Development of VLSI Devices with Flexibility and Robustness
| Project/Area Number |
25420344
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Electron device/Electronic equipment
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| Research Institution | Nihon University |
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Principal Investigator |
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| Project Period (FY) |
2013-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2013: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | デバイス / ニューロンモデル / セルラーニューラルネットワーク / ロバスト / 集積回路 / VDEC / 低容量 / 実装 / ＶＤＥＣ / 試作 |
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| Outline of Final Research Achievements |
Recent neural networks studies have been undertaken with the purpose of applying engineering to the brain function. As a method for modeling the information processing function of the brain, the artificial neural network (ANN) has been suggested. Therefore, it is necessary to construct ANN using neuron models. When a large-scale ANN is constructed, it is desired that its neuron models has a small area.
In this research, we propose the constitution of the low value capacitor implementation model. As a result, it is shown that the proposed model is able to achieve oscillation using 1 fF capacitors. Furthermore, we suggest novel model with input unit. As a result, we clarify the proposed neuron model is superior with the robustness. So, we combine the novel model with a synaptic learning model, suggested an ANN that specializes in image recognition and corresponds to high resolution images. As a result, we clarify the proposed ANN is able to recognize various input data.
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Report
(4 results)
Research Products
(40 results)
