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Shfl-BW: accelerating deep neural network inference with tensor-core aware weight pruning.

, , , , , and . DAC, page 1153-1158. ACM, (2022)

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Efficient Sparse Matrix Kernels based on Adaptive Workload-Balancing and Parallel-Reduction., , , , and . CoRR, (2021)Enabling Data Movement and Computation Pipelining in Deep Learning Compiler., , , , , , and . CoRR, (2022)Heuristic Adaptability to Input Dynamics for SpMM on GPUs., , , , , , , , and . CoRR, (2022)Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective., , , , , , and . MLSys, mlsys.org, (2022)Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective., , , , , , and . CoRR, (2021)Machine Learning for Electronic Design Automation: A Survey., , , , , , , , , and 6 other author(s). CoRR, (2021)RM-STC: Row-Merge Dataflow Inspired GPU Sparse Tensor Core for Energy-Efficient Sparse Acceleration., , , , , and . MICRO, page 338-352. ACM, (2023)LightSeq2: Accelerated Training for Transformer-Based Models on GPUs., , , , , , , and . SC, page 38:1-38:14. IEEE, (2022)TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs., , , , and . USENIX Annual Technical Conference, page 149-164. USENIX Association, (2023)GE-SpMM: general-purpose sparse matrix-matrix multiplication on GPUs for graph neural networks., , , and . SC, page 72. IEEE/ACM, (2020)