61 citations · 183 across the 21 of their papers we have counts for
38 papers
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…
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective
Hengrui Zhang, Zhongming Yu, Guohao Dai +4
Graph Neural Networks (GNNs) have been widely used in various domains, and GNNs with sophisticated computational graph lead to higher latency and larger memory consumption. Optimiz…
Paulihedral: A Generalized Block-Wise Compiler Optimization Framework For Quantum Simulation Kernels
Gushu Li, Anbang Wu, Yunong Shi +3
The quantum simulation kernel is an important subroutine appearing as a very long gate sequence in many quantum programs. In this paper, we propose Paulihedral, a block-wise compil…
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Ling Liang, Zheng Qu, Zhaodong Chen +6
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits thei…
Efficient Sparse Matrix Kernels based on Adaptive Workload-Balancing and Parallel-Reduction
Guyue Huang, Guohao Dai, Yu Wang +2
Sparse matrix-vector and matrix-matrix multiplication (SpMV and SpMM) are fundamental in both conventional (graph analytics, scientific computing) and emerging (sparse DNN, GNN) do…
A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications
Eren Kurshan, Hai Li, Mingoo Seok +1
Over the last decade, artificial intelligence has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlig…