One vision of a future artificial intelligence (AI) is where many separate units can learn
independently over a lifetime and share their knowledge with each other. The synergy …

Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal
dynamics are getting wide attention and are being applied to many relevant problems using …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …

Artificial intelligence has made people's demands for computer computing efficiency
increasingly high. The traditional hardware circuit simulation method for neural morphology …

Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep
learning by emulating the event-driven processing manner of the brain. Incorporating …

Conventional artificial intelligence (AI) systems are facing bottlenecks due to the
fundamental mismatches between AI models, which rely on parallel, in-memory, and …

The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image
segmentation processes. Thus, the precise segmentation of organs and their lesions may …