1 citations · 2 across the 7 of their papers we have counts for
7 papers
APEX-RBD: Mixed-Precision Exploration Framework for Hardware-Efficient Robot Dynamics Accelerator Design
Xingyu Liu, Hanwei Fan, Chaofang Ma +4
Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computational complexity creates a performance bottleneck that necessitates ded…
ThermoDSE: A Thermal-Aware and Comprehensive Design Space Exploration for Chiplet-Based DNN Accelerators
Jian Peng, Hanwei Fan, Jingbo Jiang +2
Chiplet-based DNN accelerators provide a scalable path to balance performance and yield for modern AI workloads. However, such systems face critical challenges in area and thermal…
HiFuzz: Hierarchical Reinforcement Learning for Semantic-Aware and Adaptive CPU Fuzzing
Ya Wang, Hanwei Fan, Zhenguo Liu +4
Modern processor verification struggles to reach deep architectural states due to the inefficiencies of traditional mutation-based fuzzing. We propose HiFuzz, a novel hierarchical…
FPPS: An FPGA-Based Point Cloud Processing System
Xiaofeng Zhou, Linfeng Du, Hanwei Fan +1
Point cloud processing is a computational bottleneck in autonomous driving systems, especially for real-time applications, while energy efficiency remains a critical system constra…
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…
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
Hanwei Fan, Ya Wang, Sicheng Li +2
With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human d…
Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning
Hanwei Fan, Jiandong Mu, Wei Zhang
Pruning is an effective technique for convolutional neural networks (CNNs) model compression, but it is difficult to find the optimal pruning policy due to the large design space.…