213 citations · 316 across the 10 of their papers we have counts for
10 papers
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks
Nan Wu, Yingjie Li, Cong Hao +3
Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapat…
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
Haoyu Wang, Nan Wu, Hang Yang +2
Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled. This work proposes an unsupervised learning framewor…
GTNet: A Tree-Based Deep Graph Learning Architecture
Nan Wu, Chaofan Wang
We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the…
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing
Nan Wu, Hang Yang, Yuan Xie +2
Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. Existing work of high-level synthesis (HLS) performance prediction usuall…
LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models
Nan Wu, Jiwon Lee, Yuan Xie +1
Despite the stride made by machine learning (ML) based performance modeling, two major concerns that may impede production-ready ML applications in EDA are stringent accuracy requi…
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation
Nan Wu, Huake He, Yuan Xie +2
Circuit design is complicated and requires extensive domain-specific expertise. One major obstacle stuck on the way to hardware agile development is the considerably time-consuming…
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
Nan Wu, Yuan Xie, Cong Hao
Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes concea…
A Survey of Machine Learning for Computer Architecture and Systems
Nan Wu, Yuan Xie
It has been a long time that computer architecture and systems are optimized for efficient execution of machine learning (ML) models. Now, it is time to reconsider the relationship…
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models
Nan Wu, Pengcheng Li
With data durability, high access speed, low power efficiency and byte addressability, NVMe and SSD, which are acknowledged representatives of emerging storage technologies, have b…
Memristor Hardware-Friendly Reinforcement Learning
Nan Wu, Adrien Vincent, Dmitri Strukov +1
Recently, significant progress has been made in solving sophisticated problems among various domains by using reinforcement learning (RL), which allows machines or agents to learn…