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Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis. Prior to ...

Heterogeneous Graph Neural Networks (HGNNs) have recently demonstrated great power in handling heterogeneous graph data, rendering them widely applied in many critical real-world domains. Most HGNN models leverage attention mechanisms to ...

Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern of HGNNs leads to the buffer thrashing issue in HGNN accelerators.

In this ...

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the ...

To accelerate time-consuming multiobjective design space exploration of CPU microarchitecture, previous work trains prediction models using a set of performance metrics derived from a few simulations, then predicts the rest. Unfortunately, the low ...

To accelerate the time-consuming multi-objective design space exploration of CPU, previous work trains prediction models using a set of cycle per instruction and power performance metrics derived from a few simulations for sampled design points, then ...