213 citations · 934 across the 47 of their papers we have counts for
5 papers · 1 filter
Heuristic Adaptability to Input Dynamics for SpMM on GPUs
Guohao Dai, Guyue Huang, Shang Yang +6
Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental components in various domains. Many previous studies exploit GPUs for SpMM acceleration because GPUs provide hi…
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
Nan Wu, Yuan Xie, Cong Hao
Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes concea…
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Xiaobing Chen, Yuke Wang, Xinfeng Xie +9
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce,…
SEALing Neural Network Models in Secure Deep Learning Accelerators
Pengfei Zuo, Yu Hua, Ling Liang +3
Deep learning (DL) accelerators are increasingly deployed on edge devices to support fast local inferences. However, they suffer from a new security problem, i.e., being vulnerable…
In-memory multiplication engine with SOT-MRAM based stochastic computing
Xin Ma, Liang Chang, Shuangchen Li +3
Processing-in-memory (PIM) turns out to be a promising solution to breakthrough the memory wall and the power wall. While prior PIM designs yield successful implementation of bitwi…