15 citations · 25 across the 7 of their papers we have counts for
7 papers
From Quarter to All: Accelerating Speculative LLM Decoding via Floating-Point Exponent Remapping and Parameter Sharing
Yushu Zhao, Yubin Qin, Yang Wang +5
Large language models achieve impressive performance across diverse tasks but exhibit high inference latency due to their large parameter sizes. While quantization reduces model si…
MoBiLE: Efficient Mixture-of-Experts Inference on Consumer GPU with Mixture of Big Little Experts
Yushu Zhao, Yubin Qin, Yang Wang +5
Mixture-of-Experts (MoE) models have recently demonstrated exceptional performance across a diverse range of applications. The principle of sparse activation in MoE models facilita…
SOFA: A Compute-Memory Optimized Sparsity Accelerator via Cross-Stage Coordinated Tiling
Huizheng Wang, Jiahao Fang, Xinru Tang +9
Benefiting from the self-attention mechanism, Transformer models have attained impressive contextual comprehension capabilities for lengthy texts. The requirements of high-throughp…
Wafer-scale Computing: Advancements, Challenges, and Future Perspectives
Yang Hu, Xinhan Lin, Huizheng Wang +12
Nowadays, artificial intelligence (AI) technology with large models plays an increasingly important role in both academia and industry. It also brings a rapidly increasing demand f…
WindMill: A Parameterized and Pluggable CGRA Implemented by DIAG Design Flow
Haojia Hui, Jiangyuan Gu, Xunbo Hu +4
With the cross-fertilization of applications and the ever-increasing scale of models, the efficiency and productivity of hardware computing architectures have become inadequate. Th…
Towards Efficient Control Flow Handling in Spatial Architecture via Architecting the Control Flow Plane
Jinyi Deng, Xinru Tang, Jiahao Zhang +9
Spatial architecture is a high-performance architecture that uses control flow graphs and data flow graphs as the computational model and producer/consumer models as the execution…
Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
Shitong Shao, Xu Dai, Lujun Li +3
Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves perf…