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- Kim, Jingook
- Integrated Circuit and Electromagnetic Compatibility Lab.
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Accuracy Investigation of a Neuromorphic Machine Learning System Due to Electromagnetic Noises Using PEEC Model
- Author(s)
- Lee, Wooryong, Kim, Jingook
- Issued Date
- 2019-10
- Citation
- IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY, v.9, no.10, pp.8718627
- Abstract
- The accuracy of a neuromorphic machine learning system was investigated using the partial equivalent element circuit (PEEC) model to analyze electromagnetic effects. A multilayer neural network (MNN) model for a classification task was introduced, and the corresponding neuromorphic circuit was designed. An efficient PEEC model for crossbar structures was proposed and validated by comparison with HFSS simulations. The designed neuromorphic circuit including the PEEC crossbar array model was simulated using SPICE while varying operation speed, structure size, and the activation function. Test cases compared electromagnetic noises and the accuracy of the neuromorphic system for the classification task.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- ISSN
- 2156-3950
- Keyword (Author)
- Accuracy, crossbar, distortion, electromagnetic effects, machine learning, neuromorphic system, partial equivalent element circuit (PEEC)
- Keyword
- NEURAL-NETWORK, MEMRISTOR, SYNAPSE, DEVICE
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