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Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses

D. Garbin 1, 2 O. Bichler 3 E. Vianello 1 Q. Rafhay 2 C. Gamrat 3 L. Perniola 1 G. Ghibaudo 2 B. Desalvo 1
1 CEA-LETI - Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information
2 IMEP-LAHC [2007-2015] - Institut de Microélectronique, Electromagnétisme et Photonique - Laboratoire d'Hyperfréquences et Caractérisation [2007-2015]
3 DACLE-LIST - Département d'Architectures, Conception et Logiciels Embarqués-LIST
Abstract : Software implementations of artificial Convolutional Neural Networks (CNNs), taking inspiration from biology, are at the state-of-the-art for Pattern Recognition (PR) applications and they are successfully used in commercial products [1]. However, they require power-hungry CPU/GPU to perform convolution operations based on computationally expensive sums of multiplications. This hinders their integration in portable devices. Some full CMOS-based hardware implementations of CNN have been suggested, but they still require the computation of multiplications [2]. In this work, we present for the first time to our knowledge a spike-based hardware implementation of CNN using HfO2 based OxRAM devices as binary synapses. OxRAM devices are chosen for their low switching energy [3] and promising endurance performance [4]. We perform an experimental and theoretical study of the impact of programming conditions at both device and system levels. A complex visual pattern recognition application is demonstrated with a spike-based hierarchical CNN, inspired from the mammalian visual cortex organization. A high accuracy (pattern recognition rate >94%) is obtained for all the tested programming conditions, even if the variability associated to weaker programming conditions is larger.
Keywords : Commercial products Convolutional neural network Hardware implementations Application programs Software implementation Mammalian visual cortex Theoretical study Visual pattern recognition Neural networks Pattern recognition Convolution Complex networks Mammals Variability tolerant Hardware Electron devices
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Conference papers
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https://hal-cea.archives-ouvertes.fr/cea-01839848
Contributor : Léna Le Roy <>
Submitted on : Monday, July 16, 2018 - 10:05:29 AM
Last modification on : Monday, July 20, 2020 - 9:12:06 AM

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D. Garbin, O. Bichler, E. Vianello, Q. Rafhay, C. Gamrat, et al.. Variability-tolerant Convolutional Neural Network for Pattern Recognition applications based on OxRAM synapses. 2014 IEEE International Electron Devices Meeting, Dec 2014, San Francisco, United States. pp.28.4.1-28.4.4, ⟨10.1109/IEDM.2014.7047126⟩. ⟨cea-01839848⟩

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