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Inherent Stochastic Learning in CMOS-Integrated HfO2 Arrays for Neuromorphic Computing
Based on the inherent stochasticity of CMOS-integrated HfO 2 -based resistive random access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is presented. For this purpose, the device-to-device variability of CMOS-integrated 4-kbit 1T-1R arrays is examined. To demonstrate th...
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Published in: | IEEE electron device letters 2019-04, Vol.40 (4), p.639-642 |
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container_title | IEEE electron device letters |
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creator | Wenger, C. Zahari, F. Mahadevaiah, M. K. Perez, E. Beckers, I. Kohlstedt, H. Ziegler, M. |
description | Based on the inherent stochasticity of CMOS-integrated HfO 2 -based resistive random access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is presented. For this purpose, the device-to-device variability of CMOS-integrated 4-kbit 1T-1R arrays is examined. To demonstrate the performance of the stochastic learning algorithm and the potential of RRAM technologies for neuro-morphic systems, a two-layer mixed-signal neural circuit for pattern recognition is implemented and tested with MNIST data. |
doi_str_mv | 10.1109/LED.2019.2900867 |
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subjects | Algorithms CMOS Gaussian distribution Hafnium compounds Hafnium oxide HfO Machine learning Memory devices neuro-morphic computing Neuromorphics Neurons Object recognition Pattern recognition Performance evaluation Random access memory RRAM Switches |
title | Inherent Stochastic Learning in CMOS-Integrated HfO2 Arrays for Neuromorphic Computing |
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