Ceil: Generalized contextual imitation learning
In this paper, we present ContExtual Imitation Learning (CEIL), a general and broadly
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …
applicable algorithm for imitation learning (IL). Inspired by the formulation of hindsight …
Design from policies: Conservative test-time adaptation for offline policy optimization
In this work, we decouple the iterative bi-level offline RL (value estimation and policy
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …
Macro placement by wire-mask-guided black-box optimization
The development of very large-scale integration (VLSI) technology has posed new
challenges for electronic design automation (EDA) techniques in chip floorplanning. During …
challenges for electronic design automation (EDA) techniques in chip floorplanning. During …
Beyond ood state actions: Supported cross-domain offline reinforcement learning
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed
data. Although avoiding the time-consuming online interactions in RL, it poses challenges …
data. Although avoiding the time-consuming online interactions in RL, it poses challenges …
Clue: Calibrated latent guidance for offline reinforcement learning
Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and
labeled datasets, which eliminates the time-consuming data collection in online RL …
labeled datasets, which eliminates the time-consuming data collection in online RL …
Offline imitation learning with variational counterfactual reasoning
In offline imitation learning (IL), an agent aims to learn an optimal expert behavior policy
without additional online environment interactions. However, in many real-world scenarios …
without additional online environment interactions. However, in many real-world scenarios …
Reinforcement Learning within Tree Search for Fast Macro Placement
Macro placement is a crucial step in modern chip design, and reinforcement learning (RL)
has recently emerged as a promising technique for improving the placement quality …
has recently emerged as a promising technique for improving the placement quality …
Addendum: A graph placement methodology for fast chip design
AlphaChip was one of the first RL methods deployed to solve a real-world engineering
problem, and its publication triggered an explosion of work on AI for chip design 2–16 …
problem, and its publication triggered an explosion of work on AI for chip design 2–16 …
Benchmarking end-to-end performance of ai-based chip placement algorithms
The increasing complexity of modern very-large-scale integration (VLSI) design highlights
the significance of Electronic Design Automation (EDA) technologies. Chip placement is a …
the significance of Electronic Design Automation (EDA) technologies. Chip placement is a …
Hardware phi-1.5 b: A large language model encodes hardware domain specific knowledge
In the rapidly evolving semiconductor industry, where research, design, verification, and
manufacturing are intricately linked, the potential of Large Language Models to revolutionize …
manufacturing are intricately linked, the potential of Large Language Models to revolutionize …