Advances in Neuromorphic Spin-Based Spiking Neural Networks: A review

Verma, Gaurav; Bindal, Namita; Nisar, Arshid; Dhull, Seema; Kaushik, Brajesh Kumar

doi:10.1109/mnano.2021.3098219

Cited by 3 publications

(3 citation statements)

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“…The memory addresses of both the program instruction and that of the data are physically separated locations in the same memory device; though, the width of the program instruction and that of processed data should be the same [300][301][302]. Moreover, the programming of these CPUs is done in such a way that these units can perform the operations in a sequential manner in which the shuttling of useful information takes place back and forth between the memory and CPU [303]. Due to this shuttling of bits, the computational speed suffers from intrinsic limitation and the usage of energy significantly increases [304].…”

Section: Rram For Neuromorphic Computingmentioning

confidence: 99%

“…The hardware implementation of neural networks demands large storage memory for computing the vector matrix product, thus making computation more energy intense. In neural networks, the input and weights are processed and stored in the form of vectors; hence, the computation is also referred to as vector matrix multiplication [303]. Figure 26a depicts the [255] biological neural network in which the biological neuron processes input information using dendrites and then transmits it to other neurons using synapses [320].…”

Section: Basics Of Neural Networkmentioning

confidence: 99%

“…b Artificial neural network. c Hardware implementation of artificial neural network using crossbar [ 303 ] …”

Section: Rram For Neuromorphic Computingmentioning

confidence: 99%

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Resistive random access memory: introduction to device mechanism, materials and application to neuromorphic computing

et al. 2023

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The modern-day computing technologies are continuously undergoing a rapid changing landscape; thus, the demands of new memory types are growing that will be fast, energy efficient and durable. The limited scaling capabilities of the conventional memory technologies are pushing the limits of data-intense applications beyond the scope of silicon-based complementary metal oxide semiconductors (CMOS). Resistive random access memory (RRAM) is one of the most suitable emerging memory technologies candidates that have demonstrated potential to replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. RRAM has grown in prominence in the recent years due to its simple structure, long retention, high operating speed, ultra-low-power operation capabilities, ability to scale to lower dimensions without affecting the device performance and the possibility of three-dimensional integration for high-density applications. Over the past few years, research has shown RRAM as one of the most suitable candidates for designing efficient, intelligent and secure computing system in the post-CMOS era. In this manuscript, the journey and the device engineering of RRAM with a special focus on the resistive switching mechanism are detailed. This review also focuses on the RRAM based on two-dimensional (2D) materials, as 2D materials offer unique electrical, chemical, mechanical and physical properties owing to their ultrathin, flexible and multilayer structure. Finally, the applications of RRAM in the field of neuromorphic computing are presented.

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