Neuromorphic computing with multi-memristive synapses

By I. Boybat, M. L. Gallo, S. Nandakumar, Timoleon Moraitis, Thomas Parnell, Tomáš Tůma, Bipin Rajendran, Y. Leblebici, Abu Sebastian, Evangelos Eleftheriou. Nature Communications, 2017

751 citations recorded by the source corpus.

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems. Memristive technology is a promising avenue towards realizing efficient non-von Neumann neuromorphic hardware. Boybat et al. proposes a multi-memristive synaptic architecture with a counter-based global arbitration scheme to address challenges associated with the non-ideal memristive device behavior.

Subfields: Artificial Intelligence, Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/677743cfbe8a71c65b6b3af31040d98aa55fb9b2

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In Artificial Intelligence

In Cellular and Molecular Neuroscience

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Citations in the corpus

Neuro-inspired computing chips (2020, methodology): “To overcome the limited precision of NVM devices, a general approach is using multiple devices with proportional significance to represent a multi-bit number 60,61 .”
Efficient and self-adaptive in-situ learning in multilayer memristor neural networks (2018, methodology): “…for other applications, by device engineering for better IV linearity 37,38 , or using pulse width instead of amplitude to represent analog input (with increased time overhead) 27,28,37 , or employing multiple memristors to represent one synaptic weight (at the expense of chip area) 52 , etc.”