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  3. openDACS开源EDA与芯片赛
OpenAtomGeneral MLAdvancedRegistration unverified

openDACS开源EDA与芯片赛

A advanced general ml competition requiring roughly 25–70 hours for an end-to-end practice run.

Difficulty
Advanced
Estimated effort
25–70 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

Advanced practitioners with experience in general machine learning. Requires strong technical foundations and comfort with complex ML pipelines.

Read the original official blurb
本赛事由openDACS工作委员会联合承办,聚焦集成电路产业的数字设计、模拟设计、芯片制造、芯片封装、处理器设计自动化、生成式芯片设计等领域,是国内集成电路产业的重要组成部分,对促进产业高质量发展具有重要意义。本赛事面向高校院所相关专业的同学、老师,以及相关行业公司专业人员,欢迎大家积极参赛,和openDACS工作委员会一起,共建开源EDA与芯片工具链。 Official category: 实战竞技赛. Organizers: openDACS工作委员会. Participants: 199.

Preparation

From registration to a first submission

  1. 01

    Python

  2. 02

    scikit-learn or similar ML library

  3. 03

    data analysis with pandas

Before you commit: Requires solid machine learning fundamentals and good experimental methodology. The competitive landscape is strong, with many experienced teams.

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
OpenAtom
Last checked
Sep 8, 2023

Entry assessment

est.
18

Prepare before joining

Overall fit estimate

Beginner fit18/100
Learning value67/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

View data stateClose
Source page verifiedMetric confirmedReviewed
Source published
Sep 8, 2023
Source created
Sep 8, 2023
Published on this site
Aug 1, 2026

Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.

reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.