142 citations · 283 across the 16 of their papers we have counts for
20 papers
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness
Xinyi Zhang, Cong Hao, Peipei Zhou +2
The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor…
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya +5
Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common ch…
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
Stefan Abi-Karam, Yuqi He, Rishov Sarkar +3
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to ubiquitous graph-related problems such as quantum chemistry, drug discovery…
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation
Nan Wu, Huake He, Yuan Xie +2
Circuit design is complicated and requires extensive domain-specific expertise. One major obstacle stuck on the way to hardware agile development is the considerably time-consuming…
WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs
Xinheng Liu, Yao Chen, Cong Hao +2
The combination of Winograd's algorithm and systolic array architecture has demonstrated the capability of improving DSP efficiency in accelerating convolutional neural networks (C…
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Susheel Suresh, Pan Li, Cong Hao +1
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning…