7 papers
LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
Liulu He, XuanAng Liu, Juntao Liu +8
Existing quantization methods are fundamentally limited by rigid, integer-based bit-widths (e.g., 2, 3-bit), resulting in a ``deployment gap" where Large Language Models cannot be…
Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists
Haohang Xu, Chengjie Liu, Qihang Wang +10
Large Language Model (LLM) exhibits great potential in designing of analog integrated circuits (IC) because of its excellence in abstraction and generalization for knowledge. Howev…
SQAP-VLA: A Synergistic Quantization-Aware Pruning Framework for High-Performance Vision-Language-Action Models
Hengyu Fang, Yijiang Liu, Yuan Du +2
Vision-Language-Action (VLA) models exhibit unprecedented capabilities for embodied intelligence. However, their extensive computational and memory costs hinder their practical dep…
BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
Liulu He, Shenli Zheng, Karwei Sun +6
Rotations have become essential to state-of-the-art quantization pipelines for large language models (LLMs) by effectively smoothing outliers in weights and activations. However, f…
DiffCkt: A Diffusion Model-Based Hybrid Neural Network Framework for Automatic Transistor-Level Generation of Analog Circuits
Chengjie Liu, Jiajia Li, Yabing Feng +5
Analog circuit design consists of the pre-layout and layout phases. Among them, the pre-layout phase directly decides the final circuit performance, but heavily depends on experien…
AnalogTester: A Large Language Model-Based Framework for Automatic Testbench Generation in Analog Circuit Design
Weiyu Chen, Chengjie Liu, Wenhao Huang +5
Recent advancements have demonstrated the significant potential of large language models (LLMs) in analog circuit design. Nevertheless, testbench construction for analog circuits r…