Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning

• URL: http://arxiv.org/abs/2402.14236v2
• Date: Mon, 29 Jul 2024 02:34:20 GMT
• Title: Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning
• Authors: Peng Gao, Tao Yu, Fei Wang, Ru-Yue Yuan,
• Abstract summary: This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Our method achieves superior performance when designing complex or rapidly evolving DFCs.
• Score: 20.500468654567033
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are not only time-consuming and labor-intensive but also rely heavily on the expertise and experience of electronics engineers, making it difficult to adapt to rapidly changing design requirements. Additionally, these commercial tools struggle with precise adjustments when parameters are sensitive to numerical changes, resulting in limited optimization effectiveness. This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Thus, it significantly reduces the subjectivity and constraints associated with circuit design. The experimental findings demonstrate clear improvements in design efficiency and quality when comparing the proposed method with traditional engineer-driven methods. Furthermore, the proposed method achieves superior performance when designing complex or rapidly evolving DFCs, highlighting the substantial potential of RL in circuit design automation. In particular, compared to the existing DFC automation design method CircuitGNN, our method achieves an average performance improvement of 8.72%. Additionally, the execution efficiency of our method is 2000 times higher than CircuitGNN on the CPU and 241 times higher on the GPU.

Related papers

• Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers [58.5711048151424]
  We introduce SPARSEK Attention, a novel sparse attention mechanism designed to overcome computational and memory obstacles. Our approach integrates a scoring network and a differentiable top-k mask operator, SPARSEK, to select a constant number of KV pairs for each query. Experimental results reveal that SPARSEK Attention outperforms previous sparse attention methods.
  arXiv  Detail & Related papers  (2024-06-24T15:55:59Z)
• Artificial-Intelligence-Based Design for Circuit Parameters of Power Converters [0.0]
  An artificial-intelligence-based design (AI-D) approach is proposed in this article for the parameter design of power converters. To mitigate human-dependence for the sake of high accuracy and easy implementation, simulation tools and batch-normalization neural network (BN-NN) are adopted. The proposed AI-D approach is validated in the circuit parameter design of the synchronous buck converter in the 48 to 12 V accessory-load power supply system in electric vehicle.
  arXiv  Detail & Related papers  (2023-07-30T08:39:41Z)
• Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning [0.0]
  A reinforcement learning approach is adapted to optimize a LLC converter at multiple operation points. The trained RL agent is able to solve new optimization problems based on LLC converter simulations.
  arXiv  Detail & Related papers  (2023-02-28T14:23:09Z)
• RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL [19.71047877921737]
  We present Robust Analog, a robust circuit design framework that involves the variation information in the optimization process. We show that Robust Analog can significantly reduce required optimization time by 14-30 times.
  arXiv  Detail & Related papers  (2022-07-13T04:06:32Z)
• 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)
• TAFA: Design Automation of Analog Mixed-Signal FIR Filters Using Time Approximation Architecture [0.0]
  A digital finite impulse response (FIR) filter design is fully synthesizable, thanks to the mature CAD support of digital circuitry. analog mixed-signal (AMS) filter design is mostly a manual process, including architecture selection, schematic design, and layout. This work presents a systematic design methodology to automate AMS FIR filter design using a time approximation architecture.
  arXiv  Detail & Related papers  (2021-12-15T01:47:35Z)
• Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization [49.207538634692916]
  We propose a new algorithm for circuit routing, named Ranking Cost, to form an efficient and trainable router. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration.
  arXiv  Detail & Related papers  (2021-10-08T07:22:45Z)
• Machine Learning Framework for Quantum Sampling of Highly-Constrained, Continuous Optimization Problems [101.18253437732933]
  We develop a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate unconstrained binary optimization problems. We showcase the framework's performance on two inverse design problems by optimizing thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering.
  arXiv  Detail & Related papers  (2021-05-06T02:22:23Z)
• Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients [51.42959998304931]
  A wide range of modern science and engineering applications are formulated as optimization problems with a system of partial differential equations (PDEs) as constraints. These PDE-constrained optimization problems are typically solved in a standard discretize-then-optimize approach. We propose a general framework to speed up PDE-constrained optimization using online neural synthetic gradients (ONSG) with a novel two-scale optimization scheme.
  arXiv  Detail & Related papers  (2021-04-25T22:43:51Z)
• Online hyperparameter optimization by real-time recurrent learning [57.01871583756586]
  Our framework takes advantage of the analogy between hyperparameter optimization and parameter learning in neural networks (RNNs) It adapts a well-studied family of online learning algorithms for RNNs to tune hyperparameters and network parameters simultaneously. This procedure yields systematically better generalization performance compared to standard methods, at a fraction of wallclock time.
  arXiv  Detail & Related papers  (2021-02-15T19:36:18Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

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.