yyh-sjtu Goto Github PK

abc-aig-to-cnf-interpretation

began-benchmarks

ICCAD2023

circuit-path-enumeration

Considering combinational logic circuit (bipartite graph) as adjacent list and enumerate all the paths from input to output. Visualise gate-level verilog code as a directed graph. Networkx library was used to draw the graphs.

decoupled-two-stage-framework

FPCB Surface Defect Detection: A Decoupled Two-Stage Object Detection Framework

deeppcb

A PCB defect dataset.

dlhsd

This Repo Contains the Source Code and Pretrained Model for the Paper "Layout Hotspot Detection with Feature Tensor Generation and Deep Biased Learning", which is published in 54th Design Automation Conference. The extension is accepted by IEEE Transactions on Computer-Aided Design.

fanoutsearch

fanoutsearch_temp

code for EDA contest

iccad-tinyml-open

[ICCAD'22 TinyML Contest] Efficient Heart Stroke Detection on Low-cost Microcontrollers

iccad16-n7m2euv

EUV Layer Hotspot Detection Benchmark Suit

iccad2019benchmarks

layout-generator

lithography-hotspot-detection

dataset Detected Hotspots in the Lithography process using Vision Transformers, Convolution Neural Networks and Artificial Neural Networks, and compared the results obtained using ANNs & CNNs

mdps_pytorch

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling （pytorch)

openabc

OpenABC-D is a large-scale labeled dataset generated by synthesizing open source hardware IPs. This dataset can be used for various graph level prediction problems in chip design.

robustify-ml-based-lithography-hotspot-detectors

This is the code base for the paper, Robustify ML-Based Lithography Hotspot Detectors [ICCAD'22].

sc2023

Fork of Lixiuran's project

tinyml_contest_iccad2022

Official code implementation of the "ACM/IEEE TinyML Design Contest" @ ICCAD 2022.

tinywebserver

:fire: Linux下C++轻量级WebServer服务器

yyh-sjtu

Config files for my GitHub profile.

yyh-sjtu.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes