In recent years, artificial intelligence (AI) has seen remarkable advancements, stretching the
limits of what is possible and opening up new frontiers. This comparative review investigates …

The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic
challenges on area, yield, and on-chip interconnection cost due to the ever-increasing …

Processing-in-memory (PIM) is a promising architecture to design various types of neural
network accelerators as it ensures the efficiency of computation together with Resistive …

On-chip communication infrastructure is a central component of modern systems-on-chip
(SoCs), and it continues to gain importance as the number of cores, the heterogeneity of …

As deep learning algorithms are widely adopted, an increasing number of them are
positioned in embedded application domains with strict reliability constraints. The …

The anticipated end of Moore's law, coupled with the breakdown of Dennard scaling,
compelled everyone to conceive forthcoming computing systems once transistors reach their …

Wireless network-on-chip (WNoC, aka WiNoC) architectures, as an emerging and viable
alternative approach, overcome the communication constraints and drawbacks of network …

As machine learning and AI continue to rapidly develop, and with the ever-closer end of
Moore's law, new avenues and novel ideas in architecture design are being created and …

Machine learning techniques, particularly those based on neural networks, are always more
often used at the edge of the network by Internet of Things (IoT) nodes. Unfortunately, the …