5 papers
GSR-GNN: Training Acceleration and Memory-Saving Framework of Deep GNNs on Circuit Graph
Yuebo Luo, Shiyang Li, Yifei Feng +3
Graph Neural Networks (GNNs) show strong promise for circuit analysis, but scaling to modern large-scale circuit graphs is limited by GPU memory and training cost, especially for d…
Late Breaking Results: Conversion of Neural Networks into Logic Flows for Edge Computing
Daniel Stein, Shaoyi Huang, Rolf Drechsler +2
Neural networks have been successfully applied in various resource-constrained edge devices, where usually central processing units (CPUs) instead of graphics processing units exis…
Layer-wise Weight Selection for Power-Efficient Neural Network Acceleration
Jiaxun Fang, Grace Li Zhang, Shaoyi Huang
Systolic array accelerators execute CNNs with energy dominated by the switching activity of multiply accumulate (MAC) units. Although prior work exploits weight dependent MAC power…
LLM-NAS: LLM-driven Hardware-Aware Neural Architecture Search
Hengyi Zhu, Grace Li Zhang, Shaoyi Huang
Hardware-Aware Neural Architecture Search (HW-NAS) requires joint optimization of accuracy and latency under device constraints. Traditional supernet-based methods require multiple…
Layer-wise dynamic rank for compressing large language models
Zhendong Mi, Bian Sun, Grace Li Zhang +1
Large language models (LLMs) have rapidly scaled in size, bringing severe memory and computational challenges that hinder their deployment. Singular Value Decomposition (SVD)-based…