Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees

• URL: http://arxiv.org/abs/2405.16951v1
• Date: Mon, 27 May 2024 08:42:42 GMT
• Title: Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees
• Authors: Davide Basso, Luca Bortolussi, Mirjana Videnovic-Misic, Husni Habal,
• Abstract summary: This paper presents an artificial intelligence driven methodology to reduce the bottleneck often encountered in the analog ICs layout phase. We frame the floorplanning problem as a Markov Decision Process and leverage reinforcement learning for automatic placement generation under established topological constraints.
• Score: 0.3749861135832073
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents an artificial intelligence driven methodology to reduce the bottleneck often encountered in the analog ICs layout phase. We frame the floorplanning problem as a Markov Decision Process and leverage reinforcement learning for automatic placement generation under established topological constraints. Consequently, we introduce Steiner tree-based methods for the global routing step and generate guiding paths to be used to connect every circuit block. Finally, by integrating these solutions into a procedural generation framework, we present a unified pipeline that bridges the divide between circuit design and verification steps. Experimental results demonstrate the efficacy in generating complete layouts, eventually reducing runtimes to 1.5% compared to manual efforts.

Related papers

• Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics [49.242224984144904]
  We propose Euphonium, a novel framework that steers generation via process reward gradient guided dynamics.<n>Our key insight is to formulate the sampling process as a theoretically principled algorithm that explicitly incorporates the gradient of a Process Reward Model.<n>We derive a distillation objective that internalizes the guidance signal into the flow network, eliminating inference-time dependency on the reward model.
  arXiv  Detail & Related papers  (2026-02-04T08:59:57Z)
• Reinforcement Learning-Based Dynamic Grouping for Tubular Structure Tracking [14.048453741483092]
  We propose a novel framework that casts segment-wise tracking as a Markov Decision Process (MDP)<n>Our method leverages Q-Learning to dynamically explore a graph of segments, computing edge weights on-demand and adaptively expanding the search space.<n> Experimental reuslts on typical tubular structure datasets demonstrate that our method significantly outperforms state-of-the-art point-wise and segment-wise approaches.
  arXiv  Detail & Related papers  (2025-06-21T11:00:17Z)
• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs) The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• Enhancing Path Planning Performance through Image Representation Learning of High-Dimensional Configuration Spaces [0.4143603294943439]
  We present a novel method for accelerating path-planning tasks in unknown scenes with obstacles. We approximate the distribution of waypoints for a collision-free path using the Rapidly-exploring Random Tree algorithm. Our experiments demonstrate promising results in accelerating path-planning tasks under critical time constraints.
  arXiv  Detail & Related papers  (2025-01-11T21:14:52Z)
• Effective Analog ICs Floorplanning with Relational Graph Neural Networks and Reinforcement Learning [0.3749861135832073]
  This paper presents a novel automatic floorplanning algorithm based on reinforcement learning. It is augmented by a relational graph convolutional neural network model for encoding circuit features and positional constraints. Our approach surpassed established floorplanning techniques in terms of speed, area and half-perimeter wire length.
  arXiv  Detail & Related papers  (2024-11-20T12:11:12Z)
• Automated Placement of Analog Integrated Circuits using Priority-based Constructive Heuristic [0.0]
  We focus on the specific class of analog placement, which requires so-called pockets, their possible merging, and parametrizable minimum distances between devices. Our solution minimizes the perimeter of the circuit's bounding box and the approximated wire length. We show the quality of the proposed method on both synthetically generated and real-life industrial instances accompanied by manually created designs.
  arXiv  Detail & Related papers  (2024-10-18T07:16:59Z)
• Finding Transformer Circuits with Edge Pruning [71.12127707678961]
  We propose Edge Pruning as an effective and scalable solution to automated circuit discovery. Our method finds circuits in GPT-2 that use less than half the number of edges compared to circuits found by previous methods. Thanks to its efficiency, we scale Edge Pruning to CodeLlama-13B, a model over 100x the scale that prior methods operate on.
  arXiv  Detail & Related papers  (2024-06-24T16:40:54Z)
• CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers [29.417270066061864]
  We present Cross-Resolution Transformer that efficiently performs cross-resolution aggregation with local attention mechanisms. We integrate two forms of Cross-Resolution Transformers into one up-sampling block for point generation, and following the coarse-to-fine manner, we construct CRA-PCN to incrementally predict complete shapes.
  arXiv  Detail & Related papers  (2024-01-03T05:57:39Z)
• Robust Stochastically-Descending Unrolled Networks [85.6993263983062]
  Deep unrolling is an emerging learning-to-optimize method that unrolls a truncated iterative algorithm in the layers of a trainable neural network. We show that convergence guarantees and generalizability of the unrolled networks are still open theoretical problems. We numerically assess unrolled architectures trained under the proposed constraints in two different applications.
  arXiv  Detail & Related papers  (2023-12-25T18:51:23Z)
• Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning [1.7087507417780985]
  We show that we can significantly reduce the size of relevant quantum circuits for trapped-ion computing. Our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.
  arXiv  Detail & Related papers  (2023-07-12T14:55:28Z)
• 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)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning [0.0]
  We propose a novel image-based learning algorithm (CBAGAN-RRT) using a Convolutional Block Attention Generative Adversarial Network. The probability distribution of the paths generated from our GAN model is used to guide the sampling process for the RRT algorithm. We train and test our network on the dataset generated by citezhang 2021 and demonstrate that our algorithm outperforms the previous state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2023-05-13T20:06:53Z)
• DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search [55.164053971213576]
  convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead. Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure. Existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space.
  arXiv  Detail & Related papers  (2020-11-04T07:43:01Z)
• Local Propagation in Constraint-based Neural Network [77.37829055999238]
  We study a constraint-based representation of neural network architectures. We investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints.
  arXiv  Detail & Related papers  (2020-02-18T16:47:38Z)

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.