Search Results for author: Hui-Ling Zhen

Found 17 papers, 4 papers with code

SoLA: Solver-Layer Adaption of LLM for Better Logic Reasoning

no code implementations19 Feb 2024 Yu Zhang, Hui-Ling Zhen, Zehua Pei, Yingzhao Lian, Lihao Yin, Mingxuan Yuan, Bei Yu

In this paper, we propose a novel solver-layer adaptation (SoLA) method, where we introduce a solver as a new layer of the LLM to differentially guide solutions towards satisfiability.

Logical Reasoning

BetterV: Controlled Verilog Generation with Discriminative Guidance

no code implementations3 Feb 2024 Zehua Pei, Hui-Ling Zhen, Mingxuan Yuan, Yu Huang, Bei Yu

In this work, we propose a Verilog generation framework, BetterV, which fine-tunes the large language models (LLMs) on processed domain-specific datasets and incorporates generative discriminators for guidance on particular design demands.

Text Generation

Machine Learning Insides OptVerse AI Solver: Design Principles and Applications

no code implementations11 Jan 2024 Xijun Li, Fangzhou Zhu, Hui-Ling Zhen, Weilin Luo, Meng Lu, Yimin Huang, Zhenan Fan, Zirui Zhou, Yufei Kuang, Zhihai Wang, Zijie Geng, Yang Li, Haoyang Liu, Zhiwu An, Muming Yang, Jianshu Li, Jie Wang, Junchi Yan, Defeng Sun, Tao Zhong, Yong Zhang, Jia Zeng, Mingxuan Yuan, Jianye Hao, Jun Yao, Kun Mao

To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.

Decision Making Management

LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation

1 code implementation28 Dec 2023 RuiZhe Zhong, Xingbo Du, Shixiong Kai, Zhentao Tang, Siyuan Xu, Hui-Ling Zhen, Jianye Hao, Qiang Xu, Mingxuan Yuan, Junchi Yan

Since circuit can be represented with HDL in a textual format, it is reasonable to question whether LLMs can be leveraged in the EDA field to achieve fully automated chip design and generate circuits with improved power, performance, and area (PPA).

Answer Generation Chatbot

DeepGate2: Functionality-Aware Circuit Representation Learning

1 code implementation25 May 2023 Zhengyuan Shi, Hongyang Pan, Sadaf Khan, Min Li, Yi Liu, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Zhufei Chu, Qiang Xu

Circuit representation learning aims to obtain neural representations of circuit elements and has emerged as a promising research direction that can be applied to various EDA and logic reasoning tasks.

Representation Learning

Conflict-driven Structural Learning Towards Higher Coverage Rate in ATPG

no code implementations4 Mar 2023 Hui-Ling Zhen, Naixing Wang, Junhua Huang, Xinyue Huang, Mingxuan Yuan, Yu Huang

(2) Conflict-driven implication and justification have been applied to increase decision accuracy and solving efficiency.

HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline

1 code implementation4 Feb 2023 Yang Li, Xinyan Chen, Wenxuan Guo, Xijun Li, Wanqian Luo, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Junchi Yan

On top of the observations that industrial formulae exhibit clear community structure and oversplit substructures lead to the difficulty in semantic formation of logical structures, we propose HardSATGEN, which introduces a fine-grained control mechanism to the neural split-merge paradigm for SAT formula generation to better recover the structural and computational properties of the industrial benchmarks.

SATformer: Transformer-Based UNSAT Core Learning

no code implementations2 Sep 2022 Zhengyuan Shi, Min Li, Yi Liu, Sadaf Khan, Junhua Huang, Hui-Ling Zhen, Mingxuan Yuan, Qiang Xu

This paper introduces SATformer, a novel Transformer-based approach for the Boolean Satisfiability (SAT) problem.

Multi-Task Learning

Machine Learning Methods in Solving the Boolean Satisfiability Problem

no code implementations2 Mar 2022 Wenxuan Guo, Junchi Yan, Hui-Ling Zhen, Xijun Li, Mingxuan Yuan, Yaohui Jin

This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques.

BIG-bench Machine Learning

Learning to Select Cuts for Efficient Mixed-Integer Programming

no code implementations28 May 2021 Zeren Huang, Kerong Wang, Furui Liu, Hui-Ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang

In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.

Multiple Instance Learning

Bilevel Learning Model Towards Industrial Scheduling

no code implementations10 Aug 2020 Longkang Li, Hui-Ling Zhen, Mingxuan Yuan, Jiawen Lu, XialiangTong, Jia Zeng, Jun Wang, Dirk Schnieders

In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.

Scheduling

Pareto Multi-Task Learning

1 code implementation NeurIPS 2019 Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qingfu Zhang, Sam Kwong

Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.

Multiobjective Optimization Multi-Task Learning

Nonlinear Collaborative Scheme for Deep Neural Networks

no code implementations4 Nov 2018 Hui-Ling Zhen, Xi Lin, Alan Z. Tang, Zhenhua Li, Qingfu Zhang, Sam Kwong

Different from them, in this paper, we aim to link the generalization ability of a deep network to optimizing a new objective function.

Unsupervised prototype learning in an associative-memory network

no code implementations10 Apr 2017 Hui-Ling Zhen, Shang-Nan Wang, Hai-Jun Zhou

Unsupervised learning in a generalized Hopfield associative-memory network is investigated in this work.

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