3 citations · 3 across the 6 of their papers we have counts for
6 papers
KV-RM: Regularizing KV-Cache Movement for Static-Graph LLM Serving
Zhiqing Zhong, Zhijing Ye, Jian Zhang +3
Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular KV-cache behavior: request le…
SDiT: Semantic Region-Adaptive for Diffusion Transformers
Bowen Lin, Fanjiang Ye, Yihua Liu +7
Diffusion Transformers (DiTs) achieve state-of-the-art performance in text-to-image synthesis but remain computationally expensive due to the iterative nature of denoising and the…
DABench-LLM: Standardized and In-Depth Benchmarking of Post-Moore Dataflow AI Accelerators for LLMs
Ziyu Hu, Zhiqing Zhong, Weijian Zheng +6
The exponential growth of large language models has outpaced the capabilities of traditional CPU and GPU architectures due to the slowdown of Moore's Law. Dataflow AI accelerators…
Globus Service Enhancements for Exascale Applications and Facilities
Weijian Zheng, Jack Kordas, Tyler J. Skluzacek +2
Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the…
GFormer: Accelerating Large Language Models with Optimized Transformers on Gaudi Processors
Chengming Zhang, Xinheng Ding, Baixi Sun +4
Heterogeneous hardware like Gaudi processor has been developed to enhance computations, especially matrix operations for Transformer-based large language models (LLMs) for generati…
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors
Chengming Zhang, Baixi Sun, Xiaodong Yu +5
Transformer models have achieved remarkable success in various machine learning tasks but suffer from high computational complexity and resource requirements. The quadratic complex…