7 citations · 9 across the 4 of their papers we have counts for
5 papers
Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design
Kourosh Hakhamaneshi, Marcel Nassar, Mariano Phielipp +2
Being able to predict the performance of circuits without running expensive simulations is a desired capability that can catalyze automated design. In this paper, we present a supe…
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi +2
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations…
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction
Kourosh Hakhamaneshi, Keertana Settaluri, Pieter Abbeel +1
In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider mul…
AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs
Keertana Settaluri, Ameer Haj-Ali, Qijing Huang +2
Domain specialization under energy constraints in deeply-scaled CMOS has been driving the need for agile development of Systems on a Chip (SoCs). While digital subsystems have desi…
BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks
Kourosh Hakhamaneshi, Nick Werblun, Pieter Abbeel +1
The discrepancy between post-layout and schematic simulation results continues to widen in analog design due in part to the domination of layout parasitics. This paradigm shift is…