1 citations · 2 across the 5 of their papers we have counts for
6 papers
ATLAS: Adaptive TDA-guided Landscape-Aware Transistor Sizing
Youngmin Oh, Jihwan Won, Yuntae Park +2
Analog transistor sizing, finding design parameters that simultaneously satisfy multiple performance specifications, is a labor-intensive bottleneck in circuit design. To support a…
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…
Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
Youngmin Oh, Jinje Park, Taejin Paik
We introduce the first variance-aware algorithms for contextual dueling bandits that leverage shallow exploration strategies with neural networks for nonlinear utility approximatio…
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling
Youngmin Oh, Jinje Park, Seunggeun Kim +3
Recent advancements in reinforcement learning (RL) for analog circuit optimization have demonstrated significant potential for improving sample efficiency and generalization across…
INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers
Souradip Poddar, Youngmin Oh, Yao Lai +3
Analog front-end design heavily relies on specialized human expertise and costly trial-and-error simulations, which motivated many prior works on analog design automation. However,…
Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
Hongjoon Ahn, Jinu Hyeon, Youngmin Oh +2
We argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Re…