视频
In the rapidly evolving landscape of Electronic Design Automation (EDA), traditional optimization tools are hitting performance ceilings, unable to effectively manage multi-objective trade-offs, generalize across design domains, or discover novel solutions. This presentation introduces an AI-powered universal optimization engine designed to overcome these limitations. Our solution enhances existing tools without replacing them, learning from response types rather than fixed functions, and handling multi-target optimization natively. to FILPAL's solution enhances existing tools without replacing them, learning from response types rather than fixed functions, and handling multi-target optimization natively.
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