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…
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design
Yanbiao Liang, Huihong Shi, Haikuo Shao +1
Recently, large language models (LLMs) have achieved huge success in the natural language processing (NLP) field, driving a growing demand to extend their deployment from the cloud…
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…
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
Huihong Shi, Haikuo Shao, Wendong Mao +1
Motivated by the huge success of Transformers in the field of natural language processing (NLP), Vision Transformers (ViTs) have been rapidly developed and achieved remarkable perf…