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The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation.
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process.
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks
The key contributions of this paper are a novel sample-efficient two-stage deep learning optimization framework leveraging RL actor-critic algorithms, and a recipe to extend it on large industrial circuits using critical device identification.
RoDesigner: Variation-Aware Optimization for Robust Analog Design with Multi-Task RL
Specifically, circuit optimizations under different variations are considered as a set of tasks.
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Our transferable optimization method makes transistor sizing and design porting more effective and efficient.
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