6 citations · 9 across the 4 of their papers we have counts for
4 papers
L-PCN: A Point Cloud Accelerator Exploiting Spatial Locality through Octree-based Islandization
Yiming Gao, Jieming Yin, Yuxiang Wang +5
Existing Point Cloud Networks (PCNs) have proven to achieve great success in many point cloud tasks such as object part segmentation, shape classification, and so on. The most popu…
Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators
Xiaochen Hao, Zijian Ding, Jieming Yin +2
Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet…
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
Ran Ran, Nuo Xu, Wei Wang +3
Recently cloud-based graph convolutional network (GCN) has demonstrated great success and potential in many privacy-sensitive applications such as personal healthcare and financial…
TENET: A Framework for Modeling Tensor Dataflow Based on Relation-centric Notation
Liqiang Lu, Naiqing Guan, Yuyue Wang +5
Accelerating tensor applications on spatial architectures provides high performance and energy-efficiency, but requires accurate performance models for evaluating various dataflow…