CktGNN: Circuit Graph Neural Network for Electronic Design Automation

• URL: http://arxiv.org/abs/2308.16406v2
• Date: Fri, 9 Feb 2024 13:38:15 GMT
• Title: CktGNN: Circuit Graph Neural Network for Electronic Design Automation
• Authors: Zehao Dong, Weidong Cao, Muhan Zhang, Dacheng Tao, Yixin Chen, Xuan Zhang
• Abstract summary: This paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing. We introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains $10$K distinct operational amplifiers. Our work paves the way toward a learning-based open-sourced design automation for analog circuits.
• Score: 67.29634073660239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit specifications. In the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit topology. By recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization subroutines. Particularly, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph basis. In this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message passing. Nonetheless, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible research. To tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains $10$K distinct operational amplifiers with carefully-extracted circuit specifications. OCB is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding datasets. Experiments on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual designs. Our work paves the way toward a learning-based open-sourced design automation for analog circuits. Our source code is available at \url{https://github.com/zehao-dong/CktGNN}.

Related papers

• Architect of the Bits World: Masked Autoregressive Modeling for Circuit Generation Guided by Truth Table [5.300504429005315]
  We propose a novel approach integrating conditional generative models with differentiable architecture search (DAS) for circuit generation. Our approach first introduces CircuitVQ, a circuit tokenizer trained based on our Circuit AutoEncoder. We then develop CircuitAR, a masked autoregressive model leveraging CircuitVQ as the tokenizer.
  arXiv  Detail & Related papers  (2025-02-18T11:13:03Z)
• Position-aware Automatic Circuit Discovery [59.64762573617173]
  We identify a gap in existing circuit discovery methods, treating model components as equally relevant across input positions. We propose two improvements to incorporate positionality into circuits, even on tasks containing variable-length examples. Our approach enables fully automated discovery of position-sensitive circuits, yielding better trade-offs between circuit size and faithfulness compared to prior work.
  arXiv  Detail & Related papers  (2025-02-07T00:18:20Z)
• LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits [17.002169206594793]
  We introduce LaMAGIC, a pioneering language model-based topology generation model. LaMAGIC can efficiently generate an optimized circuit design from the custom specification in a single pass. LaMAGIC achieves a success rate of up to 96% under a strict tolerance of 0.01.
  arXiv  Detail & Related papers  (2024-07-19T22:51:41Z)
• PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling [84.34811206119619]
  We propose a two-stage approach to pre-routing timing prediction. First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist. Second, we use a novel node updating scheme for message passing on GCN, following the topological sorting sequence of the learned graph embedding and circuit graph. Experiments on 21 real world circuits achieve a new SOTA R2 of 0.93 for slack prediction, which is significantly surpasses 0.59 by previous SOTA method.
  arXiv  Detail & Related papers  (2024-02-27T02:23:07Z)
• Circuit as Set of Points [39.14967611962792]
  We propose a novel perspective for circuit design by treating circuit components as point clouds. This approach enables direct feature extraction from raw data without any preprocessing, allows for end-to-end training, and results in high performance.
  arXiv  Detail & Related papers  (2023-10-26T14:22:43Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• CircuitQ: An open-source toolbox for superconducting circuits [0.0]
  CircuitQ is an open-source toolbox for the analysis of superconducting circuits implemented in Python. It features the automated construction of a symbolic Hamiltonian of the input circuit and a numerical representation of the Hamiltonian with a variable basis choice.
  arXiv  Detail & Related papers  (2021-06-09T19:08:33Z)
• Machine Learning Optimization of Quantum Circuit Layouts [63.55764634492974]
  We introduce a quantum circuit mapping, QXX, and its machine learning version, QXX-MLP. The latter infers automatically the optimal QXX parameter values such that the layed out circuit has a reduced depth. We present empiric evidence for the feasibility of learning the layout method using approximation.
  arXiv  Detail & Related papers  (2020-07-29T05:26:19Z)
• Training End-to-End Analog Neural Networks with Equilibrium Propagation [64.0476282000118]
  We introduce a principled method to train end-to-end analog neural networks by gradient descent. We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models. Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
  arXiv  Detail & Related papers  (2020-06-02T23:38:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.