59 citations · 119 across the 11 of their papers we have counts for
4 papers · 1 filter
COMET: Towards Partical W4A4KV4 LLMs Serving
Lian Liu, Haimeng Ren, Long Cheng +6
Quantization is a widely-used compression technology to reduce the overhead of serving large language models (LLMs) on terminal devices and in cloud data centers. However, prevalen…
SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation
Yilun Zhao, Bingmeng Wang, Wenle Jiang +4
Numerous quantum algorithms operate under the assumption that classical data has already been converted into quantum states, a process termed Quantum State Preparation (QSP). Howev…
Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation
Kaiyan Chang, Zhirong Chen, Yunhao Zhou +9
Natural language interfaces have exhibited considerable potential in the automation of Verilog generation derived from high-level specifications through the utilization of large la…
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
Kaiyan Chang, Kun Wang, Nan Yang +16
Recent advances in large language models have demonstrated their potential for automated generation of hardware description language (HDL) code from high-level prompts. Researchers…