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Found 11 papers, 4 papers with code
TransPlace: Transferable Circuit Global Placement via Graph Neural Network
Global placement, a critical step in designing the physical layout of computer chips, is essential to optimize chip performance.
Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer
In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics.
Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator
Meta-reinforcement learning (Meta-RL) has attracted attention due to its capability to enhance reinforcement learning (RL) algorithms, in terms of data efficiency and generalizability.
Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning
The Forward-Forward (FF) algorithm was recently proposed as a local learning method to address the limitations of backpropagation (BP), offering biological plausibility along with memory-efficient and highly parallelized computational benefits.
Why Antiwork: A RoBERTa-Based System for Work-Related Stress Identification and Leading Factor Analysis
r/antiwork is a subreddit for the antiwork movement, which is the desire to stop working altogether.
Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms
We executed six state-of-the-art AI-based chip placement algorithms on these designs and plugged the results of each single-point algorithm into the physical implementation workflow to obtain the final PPA results.
RoutePlacer: An End-to-End Routability-Aware Placer with Graph Neural Network
Placement is a critical and challenging step of modern chip design, with routability being an essential indicator of placement quality.
Federated reinforcement learning for robot motion planning with zero-shot generalization
This paper considers the problem of learning a control policy for robot motion planning with zero-shot generalization, i. e., no data collection and policy adaptation is needed when the learned policy is deployed in new environments.
Escaping Local Optima in Global Placement
Placement is crucial in the physical design, as it greatly affects power, performance, and area metrics.
LLM4EDA: Emerging Progress in Large Language Models for Electronic Design Automation
Since circuit can be represented with HDL in a textual format, it is reasonable to question whether LLMs can be leveraged in the EDA field to achieve fully automated chip design and generate circuits with improved power, performance, and area (PPA).
Efficient Gradient Approximation Method for Constrained Bilevel Optimization
Bilevel optimization has been developed for many machine learning tasks with large-scale and high-dimensional data.
