6 papers
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
Sungyoung Lee, Dohyeong Kim, Eshan Balachandar +2
We propose Flow-Anchored Noise-conditioned Q-Learning (FAN), a highly efficient and high-performing offline reinforcement learning (RL) algorithm. Recent work has shown that expres…
Optimize Wider, Not Deeper: Consensus Aggregation for Policy Optimization
Zelal Su, Mustafaoglu, Sungyoung Lee +3
Proximal policy optimization (PPO) approximates the trust region update using multiple epochs of clipped SGD. Each epoch may drift further from the natural gradient direction, crea…
AnalogCoder-Pro: Unifying Analog Circuit Generation and Optimization via Multi-modal LLMs
Yao Lai, Souradip Poddar, Sungyoung Lee +5
Despite recent advances, analog front-end design still relies heavily on expert intuition and iterative simulations, which limits the potential for automation. We present AnalogCod…
PPAAS: PVT and Pareto Aware Analog Sizing via Goal-conditioned Reinforcement Learning
Seunggeun Kim, Ziyi Wang, Sungyoung Lee +4
Device sizing is a critical yet challenging step in analog and mixed-signal circuit design, requiring careful optimization to meet diverse performance specifications. This challeng…
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive Pretraining
Sungyoung Lee, Ziyi Wang, Seunggeun Kim +3
Pretraining models with unsupervised graph representation learning has led to significant advancements in domains such as social network analysis, molecular design, and electronic…
AnalogCoder: Analog Circuit Design via Training-Free Code Generation
Yao Lai, Sungyoung Lee, Guojin Chen +4
Analog circuit design is a significant task in modern chip technology, focusing on the selection of component types, connectivity, and parameters to ensure proper circuit functiona…