Graph learning is becoming increasingly popular due to its superior performance in tackling
many grand challenges. While quantization is widely used to accelerate Graph Neural …

Systems for graph processing are a key enabler for insights from large-scale graphs that are
critical to many new advanced technologies such as Artificial Intelligence, Internet of Things …

Today's computing systems require moving data back-and-forth between computing
resources (eg, CPUs, GPUs, accelerators) and off-chip main memory so that computation …

This paper reviews the sequential, parallel and distributed implementations of strongly
connected component algorithms, and analyzes the challenges of each implementation in …

Graph-structure data is prevalent because of its ability to capture relations between real-
world entities. However, graph data analyzing applications, including traditional and …

Processing-in-memory (PIM) architecture is promising for accelerating deep neural network
(DNN) training due to its low-latency and energy-efficient data movement between …