14 citations · 18 across the 7 of their papers we have counts for
7 papers
Sim-FA: A GPGPU Simulator Framework for Fine-Grained Asynchronous Pipeline Analysis
Zhongchun Zhou, Yuhang Gu, Chengtao Lai +4
To efficiently support Large Language Models (LLMs), modern GPGPU architectures have introduced new features and programming paradigms, such as warp specialization. These features…
Efficient and Effective Retrieval of Dense-Sparse Hybrid Vectors using Graph-based Approximate Nearest Neighbor Search
Haoyu Zhang, Jun Liu, Zhenhua Zhu +5
ANNS for embedded vector representations of texts is commonly used in information retrieval, with two important information representations being sparse and dense vectors. While it…
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
Jinhao Li, Jiaming Xu, Shan Huang +9
Large Language Models (LLMs) have demonstrated remarkable capabilities across various fields, from natural language understanding to text generation. Compared to non-generative LLM…
MARCA: Mamba Accelerator with ReConfigurable Architecture
Jinhao Li, Shan Huang, Jiaming Xu +4
We propose a Mamba accelerator with reconfigurable architecture, MARCA.We propose three novel approaches in this paper. (1) Reduction alternative PE array architecture for both lin…
FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs
Shulin Zeng, Jun Liu, Guohao Dai +14
Transformer-based Large Language Models (LLMs) have made a significant impact on various domains. However, LLMs' efficiency suffers from both heavy computation and memory overheads…
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization
Jinhao Li, Jiaming Xu, Shiyao Li +4
Large language models (LLMs) have demonstrated impressive abilities in various domains while the inference cost is expensive. Many previous studies exploit quantization methods to…
FlashDecoding++: Faster Large Language Model Inference on GPUs
Ke Hong, Guohao Dai, Jiaming Xu +6
As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Sync…