Circuit Implementation of Convolutional Neural Network for Human Activity Recognition

Neural computing capabilities in a neuromorphic neural network circuit is studied in this work. A non-overlapped implanted (NOI) non-volatile memory device is used as a synaptic unit. The NOI array is designed to form a convolutional neural network. To verify the network circuit, recognition rates o...

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Bibliographic Details
Main Authors: Chou, Shang-Wei, Kuo, Chia-Yu, Huang, Yu-Hao, Hou, Bing-Jun, Chiang, Yueh-Lin, Jeng, Erik S.
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Convolution
Neural networks
Neuromorphics
Nonvolatile memory
Training
Online Access:Request full text
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Summary:Neural computing capabilities in a neuromorphic neural network circuit is studied in this work. A non-overlapped implanted (NOI) non-volatile memory device is used as a synaptic unit. The NOI array is designed to form a convolutional neural network. To verify the network circuit, recognition rates on six kinds of human activity are investigated. There are 12,000 data in the training set and 3,000 data in the test set. Convolution neural network circuit is first initiated by transfer-learning from simulated weights. The simulated recognition rates are 100% in training set and 98% in test set. The circuit implementation result with transfer learning on the same database is 68% in test set.
ISSN:2575-8284
DOI:10.1109/ICCE-TW52618.2021.9602894