3 papers
cs.DC2026
FalconGEMM: Surpassing Hardware Peaks with Lower-Complexity Matrix Multiplication
Honglin Zhu, Jiaping Cao, Jiang Shao +10
Peak breaking Matrix Multiplication is a promising technique to improve the performance of DL, especially in LLM training and inference. We present FalconGEMM, a cross-platform fra…
cs.AR2026
Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory
Sitian Chen, Yusen Li, Yao Chen +3
Approximate Nearest Neighbor Search (ANNS) is a core primitive in modern AI systems, and graph-based methods currently offer the best accuracy-efficiency trade-off at scale. The wo…
cs.DC2025
NM-SpMM: Accelerating Matrix Multiplication Using N:M Sparsity with GPGPU
Cong Ma, Du Wu, Zhelang Deng +11
Deep learning demonstrates effectiveness across a wide range of tasks. However, the dense and over-parameterized nature of these models results in significant resource consumption…