Weight tuning of resistive memories and convolution kernel operation on cross-point array for neuro-inspired computing

Ligang Gao, Pai Yu Chen, Shimeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Analog conductance of resistive memories is attractive for implementing the weights in neuro-inspired algorithms. One of the most popular deep learning algorithms is the convolutional neural network (CNN). In this paper, we review our recent progress on using resistive memories for neuro-inspired computing. First, we optimized the iterative programming protocol to tune the weights of HfOx based resistive memories by adjusting the pulse amplitude incremental steps, the pulse width incremental steps, and the start voltages. Then, we demonstrated the key operation in the CNN-the convolution kernel on a 12×12 cross-point array. As a proof-of-concept demonstration, we use the offline trained edge filters to detect both horizontal and vertical edges of the 50×50 pixels of a grayscale dog image. The experimental kernel operation matches the simulation results.

Original languageEnglish (US)
Title of host publication2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages247-250
Number of pages4
ISBN (Electronic)9781467397179
DOIs
StatePublished - Jul 31 2017
Event13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Hangzhou, China
Duration: Oct 25 2016Oct 28 2016

Other

Other13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016
CountryChina
CityHangzhou
Period10/25/1610/28/16

Fingerprint

Convolution
Tuning
Data storage equipment
Neural networks
Learning algorithms
Demonstrations
Pixels
Electric potential

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Electronic, Optical and Magnetic Materials

Cite this

Gao, L., Chen, P. Y., & Yu, S. (2017). Weight tuning of resistive memories and convolution kernel operation on cross-point array for neuro-inspired computing. In 2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Proceedings (pp. 247-250). [7998889] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSICT.2016.7998889

Weight tuning of resistive memories and convolution kernel operation on cross-point array for neuro-inspired computing. / Gao, Ligang; Chen, Pai Yu; Yu, Shimeng.

2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 247-250 7998889.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Gao, L, Chen, PY & Yu, S 2017, Weight tuning of resistive memories and convolution kernel operation on cross-point array for neuro-inspired computing. in 2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Proceedings., 7998889, Institute of Electrical and Electronics Engineers Inc., pp. 247-250, 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016, Hangzhou, China, 10/25/16. https://doi.org/10.1109/ICSICT.2016.7998889
Gao L, Chen PY, Yu S. Weight tuning of resistive memories and convolution kernel operation on cross-point array for neuro-inspired computing. In 2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 247-250. 7998889 https://doi.org/10.1109/ICSICT.2016.7998889
Gao, Ligang ; Chen, Pai Yu ; Yu, Shimeng. / Weight tuning of resistive memories and convolution kernel operation on cross-point array for neuro-inspired computing. 2016 13th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 247-250
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