LCM-Team
  • Datasets of Large Circuit Model
  • DeepCircuitX
    • Introduction
    • Source RTL code
    • RTL code annotations by GPT
    • Other modality information
    • RTL-Language Data for LLM Finetune
    • Data for PPA prediction
    • Tasks, experiments and results
      • LLM Finetune Results
      • PPA Prediction Results
  • ForgeEDA
    • Introduction
    • Data Preparation
    • Dataset
    • Practical Downstream Tasks
      • Practical EDA Applications
      • AI for EDA Applications
  • ForgeHLS
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  • Probability Prediction
  • Equivalent Gate Identification
  • Sub-Circuit Boundary Identification
  1. ForgeEDA
  2. Practical Downstream Tasks

AI for EDA Applications

PreviousPractical EDA Applications

Last updated 2 months ago

Probability Prediction

Task Statement

Determine the functionality of circuits by predicting the logic-1 probability of each gate under random simulation

Evaluation Metrics

Mean absolute error (MAE) between the ground truth and predicted results

Results

Models: GCN / GAT / GraphSAGE / DeepGate2 / PolarGate

Equivalent Gate Identification

Task Statement

Determine the functional similarity between two gates based on their truth tables

Evaluation Metrics

Mean absolute error (MAE) of the cosine similarity between their embeddings and the similarity between the pair-wise truth tables

Results

Models: GCN / GAT / GraphSAGE / DeepGate2 / PolarGate

Sub-Circuit Boundary Identification

Task Statement

Determine whether each gate is located on the boundaries of any adder in an AIG

Evaluation Metrics

Binary cross-entropy (BCE) loss of the node classification results (whether the boundary of full adder or not)

Results

Models: Gamora / HOGA

Input Boundary Identification

Output Boundary Identification