6 citations · 11 across the 5 of their papers we have counts for
5 papers
APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
Shaobo Ma, Chao Fang, Haikuo Shao +1
Large language models (LLMs) have revolutionized AI applications, yet their enormous computational demands severely limit deployment and real-time performance. Quantization methods…
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
Aotao Wang, Haikuo Shao, Shaobo Ma +1
State Space Models (SSMs), like recent Mamba2, have achieved remarkable performance and received extensive attention. However, deploying Mamba2 on resource-constrained edge devices…
CoDec: Prefix-Shared Decoding Kernel for LLMs
Zhibin Wang, Rui Ning, Chao Fang +12
Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critic…
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
Shaobo Ma, Chao Fang, Haikuo Shao +1
Large language models (LLMs) have been widely applied but face challenges in efficient inference. While quantization methods reduce computational demands, ultra-low bit quantizatio…
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment
Yuhao Ji, Chao Fang, Shaobo Ma +2
Transformer models have revolutionized AI tasks, but their large size hinders real-world deployment on resource-constrained and latency-critical edge devices. While binarized Trans…