GraphLeap decouples per-layer graph construction from feature updates in Vision GNNs by using previous-layer features for the current graph, enabling pipelined FPGA acceleration with up to 95.7× CPU speedup after fine-tuning.
arXiv preprint arXiv:2206.00272 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
citing papers explorer
-
GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA
GraphLeap decouples per-layer graph construction from feature updates in Vision GNNs by using previous-layer features for the current graph, enabling pipelined FPGA acceleration with up to 95.7× CPU speedup after fine-tuning.
-
ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.