This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Chip floorplanning is the engineering task of designing the physical layout of a computer
chip. Despite five decades of research 1, chip floorplanning has defied automation, requiring …

In this work, we present a learning-based approach to chip placement, one of the most
complex and time-consuming stages of the chip design process. Unlike prior methods, our …

With the recent development of deep learning technology comes the wide use of artificial
intelligence (AI) models in various domains. AI shows good performance for definite …

Scalability of graph neural networks remains one of the major challenges in graph machine
learning. Since the representation of a node is computed by recursively aggregating and …

DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …

End-to-end training of neural network solvers for graph combinatorial optimization problems
such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently …

The application of deep reinforcement learning (DRL) to computer and networked systems
has recently gained significant popularity. However, the obscurity of decisions by DRL …

We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …