The latest hardware accelerators proposed for graph applications primarily focus on graph
neural networks (GNNs) and graph mining. High-level graph reasoning tasks, such as graph …

In wireless networks, dynamic spectrum access is the key to improving spectrum utilization
and increasing channel capacity. Since the channels in wireless networks are highly …

Due to the rapidly evolving landscape of cybersecurity, the risks in securing cloud networks
and devices are attesting to be an increasingly prevalent research challenge. Reinforcement …

Hyperdimensional computing (HDC) is an emerging computing paradigm with significant
promise for efficient and robust learning. In HDC, objects are encoded with high …

Integrating deep learning with Reinforcement Learning (RL) results in algorithms that
achieve human-like learning in complex yet unknown environments via a process of trial …

During the advancement of modern deep learning algorithms, models become increasingly
demanding in computing resources and power-hungry, such that they are considered less …

Summary Vector-Symbolic Architectures (VSA, aka Hyperdimensional Computing) is an
emerging computing paradigm that works by combining vectors in a high-dimensional space …

Reinforcement learning presents a promising approach to bolster cybersecurity through the
development of intelligent agents capable of learning from their environment and adapting …

Modern intelligent applications utilizing data-driven analysis have gained significant
attention. As these applications rely on analyzing large-scale data for improved …