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Found 4 papers, 1 papers with code
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.
