Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

Spiking Neural Networks (SNNs) are an emerging computation model that uses event-
driven activation and bio-inspired learning algorithms. SNN-based machine learning …

The design of many-core neuromorphic hardware is becoming increasingly complex as
these systems are now expected to execute large machine-learning models. A predictable …

As spiking-based deep learning inference applications are increasing in embedded
systems, these systems tend to integrate neuromorphic accelerators such as µBrain to …

The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable
resistance, is a promising candidate for constructing brain-inspired computing. It operates …

Artificial intelligence (AI) and Machine Learning (ML) are becoming pervasive in today's
applications, such as autonomous vehicles, healthcare, aerospace, cybersecurity, and many …

A sustainable computing scenario demands more energy-efficient processors.
Neuromorphic systems mimic biological functions by employing spiking neural networks for …

Neuromorphic computing systems such as DYNAPs and Loihi have recently been
introduced to the computing community to improve performance and energy efficiency of …

Recently, both industry and academia have proposed many different neuromorphic
architectures to execute applications that are designed with Spiking Neural Network (SNN) …