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 …

Recent advancement in emerging brain-inspired computing has pointed out a promising
path to Machine Learning (ML) algorithms with high efficiency. Particularly, research in the …

While Graph Neural Networks (GNNs) have demonstrated remarkable achievements in
knowledge graph reasoning, their computational efficiency on conventional computing …

In this paper, we propose EdgeHD, a hierarchy-aware learning solution that performs online
training and inference in a highly distributed, cost-effective way. We use brain-inspired …

In recent times, a plethora of hardware accelerators have been put forth for graph learning
applications such as vertex classification and graph classification. However, previous works …

Objective: Develop a novel and highly efficient framework that decodes Inferior Colliculus
(IC) neural activities for phoneme recognition. Methods: We propose using …

Drawing inspiration from the outstanding learning capability of our human brains,
Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it …

Recent strides in deep learning have yielded impres-sive practical applications such as
autonomous driving, natural language processing, and graph reasoning. However, the sus …

Abstract Knowledge Graphs (KGs) have become a pivotal knowledge representation tool in
machine learning, not only providing access to existing knowledge but also enabling the …

In light of the increasing adoption of edge computing in areas such as intelligent furniture,
robotics, and smart homes, this paper introduces HyperSNN, an innovative method for …