59 citations · 119 across the 11 of their papers we have counts for
5 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…
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…
PIMCOMP: A Universal Compilation Framework for Crossbar-based PIM DNN Accelerators
Xiaotian Sun, Xinyu Wang, Wanqian Li +3
Crossbar-based PIM DNN accelerators can provide massively parallel in-situ operations. A specifically designed compiler is important to achieve high performance for a wide variety…
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
Weidong Cao, Yilong Zhao, Adith Boloor +3
Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices…