Binarized neural network of diode array with high concordance to vector–matrix multiplication
Abstract In this study, a binarized neural network (BNN) of silicon diode arrays achieved vector–matrix multiplication (VMM) between the binarized weights and inputs in these arrays. The diodes that operate in a positive-feedback loop in their p+-n-p-n+ device structure possess steep switching and b...
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Nature Portfolio
2024-03-01
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Series: | Scientific Reports |
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Online Access: | https://doi.org/10.1038/s41598-024-56575-4 |
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author | Yunwoo Shin Kyoungah Cho Sangsig Kim |
author_facet | Yunwoo Shin Kyoungah Cho Sangsig Kim |
author_sort | Yunwoo Shin |
collection | DOAJ |
description | Abstract In this study, a binarized neural network (BNN) of silicon diode arrays achieved vector–matrix multiplication (VMM) between the binarized weights and inputs in these arrays. The diodes that operate in a positive-feedback loop in their p+-n-p-n+ device structure possess steep switching and bistable characteristics with an extremely low subthreshold swing (below 1 mV) and a high current ratio (approximately 108). Moreover, the arrays show a self-rectifying functionality and an outstanding linearity by an R-squared value of 0.99986, which allows to compose a synaptic cell with a single diode. A 2 × 2 diode array can perform matrix multiply-accumulate operations for various binarized weight matrix cases with some input vectors, which is in high concordance with the VMM, owing to the high reliability and uniformity of the diodes. Moreover, the disturbance-free, nondestructive readout, and semi-permanent holding characteristics of the diode arrays support the feasibility of implementing the BNN. |
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language | English |
last_indexed | 2024-04-24T23:08:13Z |
publishDate | 2024-03-01 |
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spelling | doaj.art-83756f46aba04eaaa245de1f3c1942082024-03-17T12:22:27ZengNature PortfolioScientific Reports2045-23222024-03-011411910.1038/s41598-024-56575-4Binarized neural network of diode array with high concordance to vector–matrix multiplicationYunwoo Shin0Kyoungah Cho1Sangsig Kim2Department of Electrical Engineering, Korea UniversityDepartment of Electrical Engineering, Korea UniversityDepartment of Electrical Engineering, Korea UniversityAbstract In this study, a binarized neural network (BNN) of silicon diode arrays achieved vector–matrix multiplication (VMM) between the binarized weights and inputs in these arrays. The diodes that operate in a positive-feedback loop in their p+-n-p-n+ device structure possess steep switching and bistable characteristics with an extremely low subthreshold swing (below 1 mV) and a high current ratio (approximately 108). Moreover, the arrays show a self-rectifying functionality and an outstanding linearity by an R-squared value of 0.99986, which allows to compose a synaptic cell with a single diode. A 2 × 2 diode array can perform matrix multiply-accumulate operations for various binarized weight matrix cases with some input vectors, which is in high concordance with the VMM, owing to the high reliability and uniformity of the diodes. Moreover, the disturbance-free, nondestructive readout, and semi-permanent holding characteristics of the diode arrays support the feasibility of implementing the BNN.https://doi.org/10.1038/s41598-024-56575-4Binarized neural networkMultiply-accumulateVector–matrix multiplicationGated p+-n-p-n+ diodesPositive-feedback loop mechanism |
spellingShingle | Yunwoo Shin Kyoungah Cho Sangsig Kim Binarized neural network of diode array with high concordance to vector–matrix multiplication Scientific Reports Binarized neural network Multiply-accumulate Vector–matrix multiplication Gated p+-n-p-n+ diodes Positive-feedback loop mechanism |
title | Binarized neural network of diode array with high concordance to vector–matrix multiplication |
title_full | Binarized neural network of diode array with high concordance to vector–matrix multiplication |
title_fullStr | Binarized neural network of diode array with high concordance to vector–matrix multiplication |
title_full_unstemmed | Binarized neural network of diode array with high concordance to vector–matrix multiplication |
title_short | Binarized neural network of diode array with high concordance to vector–matrix multiplication |
title_sort | binarized neural network of diode array with high concordance to vector matrix multiplication |
topic | Binarized neural network Multiply-accumulate Vector–matrix multiplication Gated p+-n-p-n+ diodes Positive-feedback loop mechanism |
url | https://doi.org/10.1038/s41598-024-56575-4 |
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