Neural Computing and Applications

, Volume 31, Issue 11, pp 8101–8116 | Cite as

Adaptive learning rule for hardware-based deep neural networks using electronic synapse devices

  • Suhwan Lim
  • Jong-Ho Bae
  • Jai-Ho Eum
  • Sungtae Lee
  • Chul-Heung Kim
  • Dongseok Kwon
  • Byung-Gook Park
  • Jong-Ho LeeEmail author
Original Article


In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron network, we evaluate the learning performance according to various conductance responses of electronic synapse devices and weight-updating methods. It is shown that the learning accuracy is comparable to that obtained when using a software-based BP algorithm when the electronic synapse device has a linear conductance response with a high dynamic range. Furthermore, the proposed unidirectional weight-updating method is suitable for electronic synapse devices which have nonlinear and finite conductance responses. Because this weight-updating method can compensate the demerit of asymmetric weight updates, we can obtain better accuracy compared to other methods. This adaptive learning rule, which can be applied to full hardware implementation, can also compensate the degradation of learning accuracy due to the probable device-to-device variation in an actual electronic synapse device.


Deep neural networks (DNNs) Back-propagation Neuromorphic Synapse device Hardware-based deep neural networks (HW-DNNs) Classification accuracy 



This work was supported by the Korea Institute of Science and Technology (KIST) Institutional Program (Project No. 2E27810-18-P040) and the Brain Korea 21 Plus Project in 2018.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, Inter-University Semiconductor Research Center (ISRC)Seoul National UniversitySeoulRepublic of Korea

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