Machine learning (ML) models have gained prominence in solving real-world tasks.
However, implementing ML models is both compute-and memory-intensive. Domain-specific …

Graph Neural Networks (GNNs) have achieved remarkable accuracy in cognitive tasks such
as predictive analytics on graph-structured data. Hence, they have become very popular in …

This work presents a novel reconfigurable architecture for Low Latency Graph Neural
Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency …

One of the primary challenges of deploying deep neural networks (DNNs) is ensuring their
reliable performance in unpredictable edge environments, which are often disrupted by a …

Advanced computing systems have long been enablers for breakthroughs in Machine
Learning (ML) algorithms either through sheer computational power or form-factor …

ReRAM-based Computing-in-Memory (CiM) architecture has been considered a promising
solution to high-efficiency neural network accelerator, by conducting in-situ matrix …

Graph neural networks (GNNs) have emerged as powerful approaches to learn knowledge
about graphs and vertices. The rapid employment of GNNs poses requirements for …

Many real-world scientific and engineering problems can be formulated as instances of goal-
driven adaptive experimental design, wherein candidate experiments are chosen …

Action detection, which localizes and classifies activity on a video, is an important task in
many applications including autonomous vehicle, surveillance and sports analysis. Many of …

A large number of real-time artificial intelligence (AI) applications including robotics, self-
driving cars, smart health and augmented (AR)/virtual reality (VR) are enhanced/boosted by …