2 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
KVNAND: Efficient On-Device Large Language Model Inference Using DRAM-Free In-Flash Computing
Lishuo Deng, Shaojie Xu, Jinwu Chen +4
Deploying large language models (LLMs) on edge devices enables personalized agents with strong privacy and low cost. However, with tens to hundreds of billions of parameters, singl…
ISAAC: Intelligent, Scalable, Agile, and Accelerated CPU Verification via LLM-aided FPGA Parallelism
Jialin Sun, Yuchen Hu, Dean You +6
Functional verification is a critical bottleneck in integrated circuit development, with CPU verification being especially time-intensive and labour-consuming. Industrial practice…
UVLLM: An Automated Universal RTL Verification Framework using LLMs
Yuchen Hu, Junhao Ye, Ke Xu +11
Verifying hardware designs in embedded systems is crucial but often labor-intensive and time-consuming. While existing solutions have improved automation, they frequently rely on u…
MEIC: Re-thinking RTL Debug Automation using LLMs
Ke Xu, Jialin Sun, Yuchen Hu +4
The deployment of Large Language Models (LLMs) for code debugging (e.g., C and Python) is widespread, benefiting from their ability to understand and interpret intricate concepts.…