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Analogue pattern recognition with stochastic switching binary CMOS‑integrated memristive devices

  • Biological neural networks outperform todays computer technology in terms of power consumption and computing speed when associative tasks, like pattern recognition, are to be solved. The analogue and massive parallel in-memory computing in biology differs strongly with conventional transistor electronics using the von Neumann architecture. Therefore, novel bio-inspired computing architectures are recently highly investigated in the area of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are used to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology in neuromorphic systems. However, dealing with the inherent stochasticity of the resistive switching effect can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two differentBiological neural networks outperform todays computer technology in terms of power consumption and computing speed when associative tasks, like pattern recognition, are to be solved. The analogue and massive parallel in-memory computing in biology differs strongly with conventional transistor electronics using the von Neumann architecture. Therefore, novel bio-inspired computing architectures are recently highly investigated in the area of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are used to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology in neuromorphic systems. However, dealing with the inherent stochasticity of the resistive switching effect can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail and their use in a stochastic artificial neural network (StochANN) to solve the MINST pattern recognition task is examined. A mixed-signal implementation with hardware synapses and software neurons as well as numerical simulations show the proposed concept of stochastic computing is able to handle analogue data with binary memory cells.show moreshow less

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Metadaten
Author: Finn Zahari, Eduardo Perez, Mamathamba Kalishettyhalli Mahadevaiah, Hermann Kohlstedt, Christian WengerORCiD, Martin Ziegler
DOI:https://doi.org/10.1038/s41598-020-71334-x
ISSN:2045-2322
Title of the source (English):Scientific Reports
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2020
Tag:HfO2; RRAM; memristive device; neural network
Volume/Year:10
Number of pages:15
Article number:14450
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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