Assessment and improvement of the pattern recognition performance of memdiode-based cross-point arrays with randomly distributed stuck-at-faults
AuthorsAguirre, Fernando L.
Pazos, Sebastián M.
Permanent link to this recordhttp://hdl.handle.net/10754/673060
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AbstractIn this work, the effect of randomly distributed stuck-at faults (SAFs) in memristive crosspoint array (CPA)-based single and multi-layer perceptrons (SLPs and MLPs, respectively) intended for pattern recognition tasks is investigated by means of realistic SPICE simulations. The quasi-static memdiode model (QMM) is considered here for the modelling of the synaptic weights implemented with memristors. Following the standard memristive approach, the QMM comprises two coupled equations, one for the electron transport based on the double-diode equation with a single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron. By modifying the state parameter in the current-voltage characteristic, SAFs of different severeness are simulated and the final outcome is analysed. Supervised ex-situ training and two well-known image datasets involving hand-written digits and human faces are employed to assess the inference accuracy of the SLP as a function of the faulty device ratio. The roles played by the memristor’s electrical parameters, line resistance, mapping strategy, image pixelation, and fault type (stuck-at-ON or stuck-at-OFF) on the CPA performance are statistically analysed following a Monte-Carlo approach. Three different re-mapping schemes to help mitigate the effect of the SAFs in the SLP inference phase are thoroughly investigated.
CitationAguirre, F. L., Pazos, S. M., Palumbo, F., Morell, A., Suñé, J., & Miranda, E. (2021). Assessment and Improvement of the Pattern Recognition Performance of Memdiode-Based Cross-Point Arrays with Randomly Distributed Stuck-at-Faults. Electronics, 10(19), 2427. doi:10.3390/electronics10192427
SponsorsThis work has been funded by both Argentinean and European institutions. Argentine funding was provided by MINCyT (Contracts PICT2013/1210, PICT 2016/0579 and PME 2015-0196), CONICET (Project PIP-11220130100077CO) and UTN.BA (Projects PID-UTN EIUTIBA4395TC3, CCUTIBA4764TC, MATUNBA4936, CCUTNBA0006615, and CCUTNBA5182). The work of A.M., J.S. and E.M. was supported by the TEC2017-84321-C4-4-R project (Spanish Ministerio de Ciencia e Innovación). This work is also supported by the EMPIR 20FUN06 MEMQuD project with funds from the EMPIR program co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation program.S. Pazos is currently with the King Abdullah University of Science and Technology (KAUST) from Saudi Arabia.
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