Digital data, generated by corporate and individual users, is growing day by day due to a
vast range of digital applications. Magnetic hard disk drives (HDDs) currently fulfill the …

Neuromorphic computing holds promise for building next‐generation intelligent systems in a
more energy‐efficient way than the conventional von Neumann computing architecture …

Neuromorphic computing using nonvolatile memories is expected to tackle the memory wall
and energy efficiency bottleneck in the von Neumann system and to mitigate the stagnation …

To realize highly efficient neuromorphic computing that is comparable to biological
counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses …

Convolutional neural network (CNN) is a (Agrawal and Roy, IEEE Trans Magn 55: 1–7,
2019) class of deep neural network. CNNs are what we call the most representative …

Recently, brain-inspired neuromorphic computing (NC) has been gaining traction as it is
expected to be more power efficient and a more suitable platform for artificial intelligence …

Neuromorphic computing is assumed to be significantly more energy efficient than, and at
the same time expected to outperform, conventional computers in several applications, such …

Achieving multi-level devices is crucial to efficiently emulate key bio-plausible functionalities
such as synaptic plasticity and neuronal activity, and has become an important aspect of …

Neuromorphic computing (NC) architecture has shown its suitability for energy-efficient
computation. Amongst several systems, spin-orbit torque (SOT) based domain wall (DW) …

Artificial Intelligence (AI) has been gaining traction recently. However, they are executed on
devices with the von Neumann architecture, requiring high power input. Consequently, brain …