Synaptic Device Network Architecture with Feature Extraction for Unsupervised Image Classification |
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Authors: | Sungho Kim Bongsik Choi Meehyun Lim Yeamin Kim Hee‐Dong Kim Sung‐Jin Choi |
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Affiliation: | 1. Department of Electrical Engineering, Sejong University, Seoul, South Korea;2. School of Electrical Engineering, Kookmin University, Seoul, South Korea;3. Mechatronics R&D Center, Samsung Electronics, Gyonggi‐do, South Korea |
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Abstract: | For the efficient recognition and classification of numerous images, neuroinspired deep learning algorithms have demonstrated their substantial performance. Nevertheless, current deep learning algorithms that are performed on von Neumann machines face significant limitations due to their inherent inefficient energy consumption. Thus, alternative approaches (i.e., neuromorphic systems) are expected to provide more energy‐efficient computing units. However, the implementation of the neuromorphic system is still challenging due to the uncertain impacts of synaptic device specifications on system performance. Moreover, only few studies are reported how to implement feature extraction algorithms on the neuromorphic system. Here, a synaptic device network architecture with a feature extraction algorithm inspired by the convolutional neural network is demonstrated. Its pattern recognition efficacy is validated using a device‐to‐system level simulation. The network can classify handwritten digits at up to a 90% recognition rate despite using fewer synaptic devices than the architecture without feature extraction. |
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Keywords: | carbon nanotubes feature extraction image classification neuromorphic systems recognition rates |
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