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Author: Kohlstedt, Hermann × Has files: Yes × search.filters.applied.f.series: Scientific Reports 10 (2020) ×

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    Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba K.; Perez, Eduardo; Kohlstedt, Hermann; Wenger, Christian
    Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s).
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    Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zahari, Finn; Pérez, Eduardo; Mahadevaiah, Mamathamba Kalishettyhalli; Kohlstedt, Hermann; Wenger, Christian; Ziegler, Martin
    Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed 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 to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching 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 potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells. © 2020, The Author(s).