The unprecedented progress in artificial intelligence (AI), particularly in deep learning
algorithms with ubiquitous internet connected smart devices, has created a high demand for …

Spiking Neural Networks (SNNs) are a promising research direction for building power-
efficient information processing systems, especially for temporal tasks such as speech …

In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems
(AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive …

Neuromorphic processors aim to emulate the biological principles of the brain to achieve
high efficiency with low power consumption. However, the lack of flexibility in most …

The role of axonal synaptic delays in the efficacy and performance of artificial neural
networks has been largely unexplored. In step-based analog-valued neural network models …

Sparse and event-driven spiking neural network (SNN) algorithms are the ideal candidate
solution for energy-efficient edge computing. Yet, with the growing complexity of SNN …

Computation-in-Memory (CIM) is an emerging computing paradigm to address memory
bottleneck challenges in computer architecture. A CIM unit cannot fully replace a general …

Neuromorphic processors promise low-latency and energy-efficient processing by adopting
novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle …

Spiking neural networks (SNNs) for event-based optical flow are claimed to be
computationally more efficient than their artificial neural networks (ANNs) counterparts, but a …

Spiking neural network (SNN) distinguish themselves from artificial neural network (ANN)
because of their inherent temporal processing and spike-based computations, enabling a …