3 papers
cs.AR2025
FLEX: Leveraging FPGA-CPU Synergy for Mixed-Cell-Height Legalization Acceleration
Xingyu Liu, Jiawei Liang, Linfeng Du +5
In this work, we present FLEX, an FPGA-CPU accelerator for mixed-cell-height legalization tasks. We address challenges from the following perspectives. First, we optimize the task…
cs.AR2025
DRACO: Co-design for DSP-Efficient Rigid Body Dynamics Accelerator
Xingyu Liu, Jiawei Liang, Yipu Zhang +5
We propose a hardware-efficient RBD accelerator based on FPGA, introducing three key innovations. First, we propose a precision-aware quantization framework that reduces DSP demand…
cs.AR2025
HERO: Hardware-Efficient RL-based Optimization Framework for NeRF Quantization
Yipu Zhang, Chaofang Ma, Jinming Ge +3
Neural Radiance Field (NeRF) has emerged as a promising 3D reconstruction method, delivering high-quality results for AR/VR applications. While quantization methods and hardware ac…