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ReRAM-Based Neuromorphic Computing

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Frontiers of Quality Electronic Design (QED)

Abstract

Neuromorphic computing systems are faster and more energy efficient compared to von Neumann computing architectures because of their ability to emulate biological systems. However, hardware implementations of neural networks require large amounts of area and memory and have limitations on scalability. With their highly parallelized computing capabilities and high integration densities, memristors are an energy-efficient solution to this problem. Due to their similarities with biological synapses, memristors have gained immense attention as building blocks in neuromorphic systems. A memristor is a two-terminal nonvolatile memory device that operates like a variable resistor and can be used to form large crossbar arrays that are capable of performing data-intensive calculations and in-memory computations. This makes memristors a promising candidate for brain-inspired computing. Many such emerging nonvolatile memory (eNVM) technologies have been developed over the years that include phase-change random access memory (PCRAM), magnetic random access memory (MRAM), and resistive random access memory (ReRAM). ReRAM, which is also referred to as a memristor, is a filament-type device that changes its resistance based on the voltage applied and has been immensely researched by many companies and research groups. The goal of this chapter is to deliver the merits and characteristics of ReRAM devices. This chapter will initially provide a brief overview of the working mechanism of a memristor and discuss how ReRAMs use the switching process of memristors. The chapter will dive more into detail on the construction of ReRAM crossbar and the read and write operations. Furthermore, the recent advancements in ReRAM on the area of neuromorphic computing will be covered including its developments in multilayer perceptron (MLP), spiking neural networks (SNN), convolutional neural networks (CNN), and recurrent neural networks (RNN). This topic will be useful in providing information on future prospects of using ReRAM devices in the field of neuromorphic computing. Through this chapter, readers will be able to gain a deeper understanding about the future of ReRAM and how they pave the way for deep learning accelerators and edge devices. With the use of these ReRAM-based architectures, the issue of latency, power consumption, and memory bottleneck can be mitigated, facilitating for brain-inspired computing.

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Authors and Affiliations

  1. Virginia Tech, Blacksburg, VA, USA

    Fabiha Nowshin & Yang Yi

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  1. Fabiha Nowshin
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  2. Yang Yi
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Correspondence to Fabiha Nowshin .

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  1. Silicon Valley Polytechnic Institute, Los Altos Hills, CA, USA

    Ali Iranmanesh

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Nowshin, F., Yi, Y. (2023). ReRAM-Based Neuromorphic Computing. In: Iranmanesh, A. (eds) Frontiers of Quality Electronic Design (QED). Springer, Cham. https://doi.org/10.1007/978-3-031-16344-9_2

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  • DOI: https://doi.org/10.1007/978-3-031-16344-9_2

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