Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have
achieved a substantial scientific leap in the past decade. However, even with the advent of …

In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

Neuromorphic computing (NC) architecture inspired by biological nervous systems has
been actively studied to overcome the limitations of conventional von Neumann …

As data processing volume increases, the limitations of traditional computers and the need
for more efficient computing methods become evident. Neuromorphic computing mimics the …

Nanomagnetic and spintronic devices, which make use of physical phenomena in materials
and interfaces like perpendicular magnetic anisotropy (PMA) and spin–orbit torque (SOT) to …

Focus in deep neural network hardware research for reducing latencies of memory fetches
has steered in the direction of analog-based artificial neural networks (ANN). The promise of …

The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

Transformers have achieved great success in a wide variety of natural language processing
(NLP) tasks due to the self-attention mechanism, which assigns an importance score for …