BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation
- URL: http://arxiv.org/abs/2401.10753v1
- Date: Fri, 19 Jan 2024 15:22:28 GMT
- Title: BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation
- Authors: Yingjie Li, Anthony Agnesina, Yanqing Zhang, Haoxing Ren, Cunxi Yu
- Abstract summary: This work presents BoolGebra, a novel attributed graph-learning approach for Boolean algebraic manipulation.
BoolGebra incorporates Graph Neural Networks (GNNs) and takes initial feature embeddings from both structural and functional information as inputs.
Experiments involve training the BoolGebra model w.r.t design-specific and cross-design inferences using the trained model.
BoolGebra is integrated with existing synthesis tool ABC to perform end-to-end logic minimization evaluation w.r.t SOTA baselines.
- Score: 14.222330464131089
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Boolean algebraic manipulation is at the core of logic synthesis in
Electronic Design Automation (EDA) design flow. Existing methods struggle to
fully exploit optimization opportunities, and often suffer from an explosive
search space and limited scalability efficiency. This work presents BoolGebra,
a novel attributed graph-learning approach for Boolean algebraic manipulation
that aims to improve fundamental logic synthesis. BoolGebra incorporates Graph
Neural Networks (GNNs) and takes initial feature embeddings from both
structural and functional information as inputs. A fully connected neural
network is employed as the predictor for direct optimization result
predictions, significantly reducing the search space and efficiently locating
the optimization space. The experiments involve training the BoolGebra model
w.r.t design-specific and cross-design inferences using the trained model,
where BoolGebra demonstrates generalizability for cross-design inference and
its potential to scale from small, simple training datasets to large, complex
inference datasets. Finally, BoolGebra is integrated with existing synthesis
tool ABC to perform end-to-end logic minimization evaluation w.r.t SOTA
baselines.
Related papers
- Learning from Linear Algebra: A Graph Neural Network Approach to Preconditioner Design for Conjugate Gradient Solvers [42.69799418639716]
Deep learning models may be used to precondition residuals during iteration of such linear solvers as the conjugate gradient (CG) method.
Neural network models require an enormous number of parameters to approximate well in this setup.
In our work, we recall well-established preconditioners from linear algebra and use them as a starting point for training the GNN.
arXiv Detail & Related papers (2024-05-24T13:44:30Z) - Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning [89.89857766491475]
We propose a complex reasoning schema over KG upon large language models (LLMs)
We augment the arbitrary first-order logical queries via binary tree decomposition to stimulate the reasoning capability of LLMs.
Experiments across widely used datasets demonstrate that LACT has substantial improvements(brings an average +5.5% MRR score) over advanced methods.
arXiv Detail & Related papers (2024-05-02T18:12:08Z) - Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network [10.556366638048384]
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations.
Previous studies attempt to reduce the computational budget by leveraging graph-level or network-level sparsification techniques.
We propose Unifews, which unifies the two operations in an entry-wise manner considering individual matrix elements.
arXiv Detail & Related papers (2024-03-20T03:07:30Z) - Universal Neural Functionals [67.80283995795985]
A challenging problem in many modern machine learning tasks is to process weight-space features.
Recent works have developed promising weight-space models that are equivariant to the permutation symmetries of simple feedforward networks.
This work proposes an algorithm that automatically constructs permutation equivariant models for any weight space.
arXiv Detail & Related papers (2024-02-07T20:12:27Z) - 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) - Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet
Energy [103.74640329539389]
We propose a deep FS method that simultaneously conducts feature selection and differentiable $ k $-NN graph learning.
We employ Optimal Transport theory to address the non-differentiability issue of learning $ k $-NN graphs in neural networks.
We validate the effectiveness of our model with extensive experiments on both synthetic and real-world datasets.
arXiv Detail & Related papers (2023-05-21T08:15:55Z) - An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws [24.356906682593532]
We study the compute-optimal trade-off between model and training data set sizes for large neural networks.
Our result suggests a linear relation similar to that supported by the empirical analysis of chinchilla.
arXiv Detail & Related papers (2022-12-02T18:46:41Z) - Recurrent Bilinear Optimization for Binary Neural Networks [58.972212365275595]
BNNs neglect the intrinsic bilinear relationship of real-valued weights and scale factors.
Our work is the first attempt to optimize BNNs from the bilinear perspective.
We obtain robust RBONNs, which show impressive performance over state-of-the-art BNNs on various models and datasets.
arXiv Detail & Related papers (2022-09-04T06:45:33Z) - Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction [22.974348682859322]
We propose a framework that can directly learn embeddings for the given netlist to enhance the quality of our node features.
By combining the learned embedding on top of the netlist with the GNNs, our method improves prediction performance, generalizes to new circuit lines, and is efficient in training, potentially saving over $90 %$ of runtime.
arXiv Detail & Related papers (2021-11-10T20:56:29Z) - Relative gradient optimization of the Jacobian term in unsupervised deep
learning [9.385902422987677]
Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.
Deep density models have been widely used for this task, but their maximum likelihood based training requires estimating the log-determinant of the Jacobian.
We propose a new approach for exact training of such neural networks.
arXiv Detail & Related papers (2020-06-26T16:41:08Z) - Predictive Coding Approximates Backprop along Arbitrary Computation
Graphs [68.8204255655161]
We develop a strategy to translate core machine learning architectures into their predictive coding equivalents.
Our models perform equivalently to backprop on challenging machine learning benchmarks.
Our method raises the potential that standard machine learning algorithms could in principle be directly implemented in neural circuitry.
arXiv Detail & Related papers (2020-06-07T15:35:47Z)
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.