142 citations · 286 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
Yao Chen, Cole Hawkins, Kaiqi Zhang +2
The deep neural network (DNN) based AI applications on the edge require both low-cost computing platforms and high-quality services. However, the limited memory, computing resource…
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
Yuhong Li, Cong Hao, Xiaofan Zhang +5
High quality AI solutions require joint optimization of AI algorithms and their hardware implementations. In this work, we are the first to propose a fully simultaneous, efficient…