CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis

• URL: http://arxiv.org/abs/2407.10977v1
• Date: Thu, 6 Jun 2024 01:59:59 GMT
• Title: CIRCUITSYNTH: Leveraging Large Language Models for Circuit Topology Synthesis
• Authors: Prashanth Vijayaraghavan, Luyao Shi, Ehsan Degan, Xin Zhang,
• Abstract summary: We introduce CIRCUITSYNTH, a novel approach that harnesses LLMs to facilitate the automated synthesis of valid circuit topologies. Our approach lays the foundation for future research aimed at enhancing circuit efficiency and specifying output voltage.
• Score: 7.131266114437393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Circuit topology generation plays a crucial role in the design of electronic circuits, influencing the fundamental functionality of the circuit. In this paper, we introduce CIRCUITSYNTH, a novel approach that harnesses LLMs to facilitate the automated synthesis of valid circuit topologies. With a dataset comprising both valid and invalid circuit configurations, CIRCUITSYNTH employs a sophisticated two-phase methodology, comprising Circuit Topology Generation and Circuit Topology Refinement. Experimental results demonstrate the effectiveness of CIRCUITSYNTH compared to various fine-tuned LLM variants. Our approach lays the foundation for future research aimed at enhancing circuit efficiency and specifying output voltage, thus enabling the automated generation of circuit topologies with improved performance and adherence to design requirements.

Related papers

• 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)
• Functional Faithfulness in the Wild: Circuit Discovery with Differentiable Computation Graph Pruning [14.639036250438517]
  We introduce a comprehensive reformulation of the task known as Circuit Discovery, along with DiscoGP. DiscoGP is a novel and effective algorithm based on differentiable masking for discovering circuits.
  arXiv  Detail & Related papers  (2024-07-04T09:42:25Z)
• Logic Synthesis with Generative Deep Neural Networks [20.8279111910994]
  We introduce a logic synthesis rewriting operator based on the Circuit Transformer model, named "ctrw" (Circuit Transformer Rewriting) We propose two-stage training scheme for the Circuit Transformer tailored for logic, with iterative improvement of optimality through self-improvement training. We also integrate the Circuit Transformer with state-of-the-art rewriting techniques to address scalability issues, allowing for guided DAG-aware rewriting.
  arXiv  Detail & Related papers  (2024-06-07T07:16:40Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• CktGNN: Circuit Graph Neural Network for Electronic Design Automation [67.29634073660239]
  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.
  arXiv  Detail & Related papers  (2023-08-31T02:20:25Z)
• Adaptive Planning Search Algorithm for Analog Circuit Verification [53.97809573610992]
  We propose a machine learning (ML) approach, which uses less simulations. We show that the proposed approach is able to provide OCCs closer to the specifications for all circuits.
  arXiv  Detail & Related papers  (2023-06-23T12:57:46Z)
• Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection [88.23337313766353]
  This work first provides a comprehensive statistical theory for transformers to perform ICL. We show that transformers can implement a broad class of standard machine learning algorithms in context. A emphsingle transformer can adaptively select different base ICL algorithms.
  arXiv  Detail & Related papers  (2023-06-07T17:59:31Z)
• Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization [6.599793419469274]
  This paper presents a reinforcement learning method to automate the analog circuit parameter optimization. It is inspired by human experts who rely on domain knowledge of analog circuit design. Experimental results on exemplary circuits show it achieves human-level design accuracy (99%) 1.5X efficiency of existing best-performing methods.
  arXiv  Detail & Related papers  (2022-04-27T13:58:51Z)
• 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)
• Thermal phase shifters for femtosecond laser written photonic integrated circuits [58.720142291102135]
  Photonic integrated circuits (PICs) are acknowledged as an effective solution to fulfill the demanding requirements of many practical applications in both classical and quantum optics. Phase shifters integrated in the photonic circuit offer the possibility to dynamically reconfigure its properties in order to fine tune its operation or to produce adaptive circuits. We show how thermal shifters to reconfigure photonic circuits can be solved by a careful design of the thermal shifters and by choosing the most appropriate way to drive them.
  arXiv  Detail & Related papers  (2020-04-23T20:09:56Z)

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.