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Designing Neuromorphic Computing Systems with Memristor Devices

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Abstract

Neuromorphic computing systems are under heavy investigation as a potential substitute for the traditional von Neumann systems in high-speed low-power applications. One way to implement neuromorphic systems in hardware is to use the new emerging devices such as Resistive RAM (ReRAM or Memristor), because of the promising features these devices provide, such as low feature size, extremely low power consumption, synaptic like behavior, and scalability. However, these systems are in their early developing stages and still have many challenges to be solved before they can be mature enough for commercialization. In this work, we are going to investigate hardware implementation of neuromorphic systems. Specifically, this work will study hardware implementation for two types of neural networks; feed forward neuromorphic systems and Echo State Network (ESN) model, as a special type of Recurrent Neural Networks (RNNs). In addition, detailed design procedure for designing and simulating the proposed architecture, along with a detailed system evaluation will be provided.

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Correspondence to Amr Mahmoud Hassan .

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Hassan, A.M., Liu, C., Yang, C., (Helen) Li, H., Chen, Y. (2019). Designing Neuromorphic Computing Systems with Memristor Devices. In: Chua, L., Sirakoulis, G., Adamatzky, A. (eds) Handbook of Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-76375-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-76375-0_16

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-76375-0

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