Datasets:

AiEDA
/

iPCL-R

license: gpl-3.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
dataset_info:
  features:
    - name: net_name
      dtype: string
    - name: driver
      dtype: string
    - name: loads
      list: string
    - name: tree_seq
      list: string
    - name: connected_info
      list:
        - name: driver
          dtype: string
        - name: loads
          list: string
        - name: net_name
          dtype: string
        - name: overlap_volume
          dtype: int64
    - name: overlap_info
      list:
        - name: driver
          dtype: string
        - name: loads
          list: string
        - name: net_name
          dtype: string
        - name: overlap_volume
          dtype: int64
    - name: source_design
      dtype: string
  splits:
    - name: train
      num_bytes: 14242624501
      num_examples: 6347092
    - name: validation
      num_bytes: 24439501
      num_examples: 10163
  download_size: 3468684189
  dataset_size: 14267064002

This is dataset for project iPCL-R.

iPCL-R A Pre-training foundation model for Chip Layout Routing

Demo of iPCL-R

iPCL-R Demo A chip layout generation demo by iPCL-R

Project Overview

iPCL-R Framework The framework of iPCL-R. (a) Dataset. (b) Symbolic system. (c) Pre-training. (d) Inference.

iPCL-R addresses the challenge of automated routing pattern generation in chip design by treating routing patterns as sequences that can be learned and generated by large language models. The project implements a complete pipeline from EDA data extraction to model training and routing generation.

Demo of iPCL-R
Project Overview
- Table of Contents
- Key Innovation
Architecture
Core Modules
Quick Start
- Prerequisites
- Basic Usage
Research Applications
Evaluation Metrics
Contributing
Citation

Key Innovation

Domain-specific tokenization optimized for spatial reasoning tasks in chip routing
Transformer-based sequence-to-sequence models for routing pattern generation
Comprehensive evaluation metrics combining NLP and routing-specific measures

Architecture

The project is organized into four main modules:

iPCL-R/
├── flow/              # [Main] 3-stage ML pipeline (tokenization → training → evaluation)
├── data_synthesis/    # [Optional] EDA data processing and ML-ready dataset generation
├── experiments/       # [Optional] Experimental analysis and validation studies
└── third_party/       # EDA tool integration and chip design automation

Core Modules

Flow Module

Purpose: Complete 3-stage pipeline for routing pattern generation

Pipeline Stages:

Stage 1: Tokenization

UnifiedTokenizer: Supports 5 algorithms (DecimalWordLevel, Seg-BPE, Concat-BPE, Seg-BBPE, Concat-BBPE)
Direction Encoding: Converts 3D coordinates to directional tokens (R/L/U/D/T/B)
Tree Structure: Handles routing trees with PUSH/POP and BRANCH/END tokens
Special Tokens: BOS/EOS, PAD, DRIVER/LOAD for routing semantics

Stage 2: Training

Custom Architecture: T5-Gemma encoder-decoder transformer
Multi-optimizer Support: AdamW, Adafactor, Lion optimizers
Distributed Training: HuggingFace Accelerate integration
Monitoring: TensorBoard and comprehensive logging

Stage 3: Evaluation

Multi-metric Assessment: NLP metrics (ROUGE, BLEU) + routing-specific (RED)
Validation Pipeline: Coordinate parsing, tree structure analysis
EDA Integration: DEF format output for industry tool verification

Data Synthesis Module

Purpose: Convert EDA design data into large model learning-ready formats

Key Components:

MetadataTracker & DataGenerator: Base infrastructure for data generation with Parquet output
NetPatternProcessor: Processes wire routing patterns and calculates directions
NetGraphProcessor: Converts routing networks into NetworkX graphs
DesignGraphProcessor: Creates design-level graphs with spatial overlap detection
DatasetAggregator: Consolidates data into HuggingFace Dataset format

Data Types Generated:

net_seqs: Network sequence representations with driver/load information
pin2pin_pattern_seqs: Pin-to-pin routing pattern sequences
pin2pin_loc_seqs: Spatial location sequences for routing paths
design_graph: Design-level connectivity and overlap graphs

Ready-made online datasets:

iPCL-R Dataset on HuggingFace

Experiments Module

Purpose: Validation and optimization studies for the pipeline

Research Areas:

Tokenization Comparison: Statistical analysis of 5 tokenization algorithms across multiple vocabulary sizes
Model Architecture Studies: Parameter count vs. performance analysis (small/medium/large variants)
LLM Fine-tuning: Supervised fine-tuning with LoRA, multi-GPU support, comprehensive evaluation
Symbol Analysis: Domain-specific vs. human language tokenization comparison
Feature Ablation: Input feature importance analysis (planned)

Key Methodologies:

Multi-stage filtering for coordinate validation
Tree structure integrity checking
Statistical significance testing
Comprehensive visualization and reporting

AiEDA Third-Party Module

Purpose: Extract features (metadata) from physical design files and write the generated results back to the physical design file (DEF)

Integration: Provides EDA backend infrastructure and large model data generation for the iPCL-R project.

Quick Start

Prerequisites

# Install dependencies
pip install -r requirements.txt

# Key dependencies include:
# torch, transformers, datasets, accelerate, networkx, pandas, tqdm

Basic Usage

Pipeline Execution

# Generate pipeline configuration
python -m flow.pipeline_init --create-flow-config config.json

# Read the config guide in 'flow/README.md'
vim config.json

# Execute 3-stage pipeline
python -m flow.launch_tokenization --flow-config config.json
# Configure your accelerate settings as needed (e.g., run 'accelerate config' for initial setup)
accelerate launch -m flow.launch_training --flow-config config.json  
accelerate launch -m flow.launch_evaluation --flow-config config.json

Dataset Construction

If you wish to build your own dataset, please organize your data following the AiEDA framework (I sincerely do not recommend this, as it involves extensive engineering dependencies), and utilize the 'data_synthesis' module of this project to assist in the construction of the dataset.

python -m data_synthesis.main_aggregation

Experimental Analysis

The experiments and the code for generating the figures in this work are located in the 'experiments' directory. Due to issues such as log extraction and path dependencies, it is not convenient for users to run them with a single click. Therefore, this module is for reference only.

Research Applications

iPCL-R enables research in:

Spatial AI: Neural networks for 2D/3D coordinate reasoning
Domain-specific Tokenization: Optimization for spatial/geometric data
EDA Automation: AI-driven chip design optimization
Graph Neural Networks: Routing network topology analysis
Transfer Learning: Pre-trained models for chip design tasks

Evaluation Metrics

NLP Metrics: ROUGE, BLEU, exact match for sequence similarity
Routing Metrics: RED (Routing Edit Distance), coordinate accuracy
Structural Metrics: Tree validation, connectivity analysis
Quality Metrics: Manufacturability, design rule compliance