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RRAM-Based Neuromorphic Computing Systems

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Emerging Non-volatile Memory Technologies

Abstract

New computational paradigms have been widely investigated in order to further improve the approach in handling the exponentially increasing amount of data generated across the globe as well as various emerging hardware requirements to execute complex tasks, e.g., pattern recognition, speech classification, etc. Neuromorphic computing has emerged as one of the most extensively investigated among these approaches. RRAM devices with their desired characteristics have been rigorously engineered to meet the synaptic element requirements to realize such robust yet power efficient platform. Among the performance parameters necessary to achieve an ideal synaptic device in context of RRAM device, there are certain dependency and potential trade-offs. In this chapter, different type of RRAM, i.e., anion and cation, devices based on their underlying physical mechanism with various advantages and disadvantages are discussed. Different techniques that have been implemented to improve the device synaptic characteristics from material viewpoint and programming approach followed by several system level simulations demonstrating the projected performance of these devices are provided in detail. Different algorithms available for the RRAM synapse implementation are also discussed.

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  1. Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Jurong West, 637371, Singapore

    Putu Andhita Dananjaya & Wen Siang Lew

  2. Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Fusionopolis Way, 138632, Singapore

    Roshan Gopalakrishnan

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  1. School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Wen Siang Lew

  2. School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Gerard Joseph Lim

  3. School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Putu Andhita Dananjaya

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Dananjaya, P.A., Gopalakrishnan, R., Lew, W.S. (2021). RRAM-Based Neuromorphic Computing Systems. In: Lew, W.S., Lim, G.J., Dananjaya, P.A. (eds) Emerging Non-volatile Memory Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-6912-8_12

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