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TARGet introduces a novel fusion-based architecture that integrates Graph Neural Networks (GNNs) and sub-circuit connectivity-aware neural networks to improve data efficiency and achieves 9.7x higher prediction accuracy under a strict 1% error threshold relative to SOTA models.
ORACLE is an open-source RL-based framework for MO analog circuit design optimization that replaces scalar reward optimization with vector-valued learning and preference-aware conditioning, and incorporates a large language model (LLM)-guided action selection mechanism to filter actions that are likely to lead to subop...
An open-source end-to-end multi-agent LLM workflow that takes user specs as input and outputs the appropriate netlist, encompassing both topology generation and circuit sizing, and achieves a figure of merit (FoM) comparable to that of known topologies, and up to 3x higher for certain circuits.
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