Abstract Hardware-aware Neural Architecture Search (HW-NAS) is increasingly being used
to design efficient deep learning architectures. An efficient and flexible search space is …

Abstract Generative Adversarial Network (GAN) has been widely used in many research
areas of computer vision, anomaly detection, translation, optimal control, etc. However, in …

In recent years, there has been a significant advancement in memristor-based neural
networks, positioning them as a pivotal processing-in-memory deployment architecture for a …

Abstract Selecting and optimizing Convolutional Neural Networks (CNNs) has become a
very complex task given the number of associated optimizable parameters, as well as the …

Latest insights from biology show that intelligence not only emerges from the connections
between neurons but that individual neurons shoulder more computational responsibility …

A continual learning agent builds on previous experiences to develop increasingly complex
behaviors by adapting to non-stationary and dynamic environments while preserving …

Abstract Neural Architecture Search (NAS) is a fast-developing research field to promote
automatic machine learning. Among the recently popular NAS methods, one-shot NAS has …

Designing efficient deep learning architectures is a challenging task that requires balancing
performance and hardware efficiency. Neural Architecture Search (NAS) has emerged as a …

Deep learning has achieved remarkable success across various domains, leading to the
development of increasingly complex and resource-intensive models. For that, these models …