AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI
arXiv:2507.15104v2 Announce Type: replace-cross Abstract: Recent advances in generative AI (GenAI) have shown transformative potential for modern hardware design. However, existing GenAI-driven approaches fall short of enabling large-scale electronic design automation (EDA) due to the proprietary and siloed nature of hardware datasets, which cannot be centralized for model training. Achieving at-scale GenAI-driven EDA, therefore, requires a novel privacy-preserving framework that can leverage distributed data without compromising confidentiality. This work introduces AnalogFed, the first priva
The increasing maturity of generative AI combined with the inherent privacy and proprietary nature of hardware design data necessitates new frameworks for collaborative development.
This breakthrough addresses a critical bottleneck in applying generative AI to complex hardware design, enabling large-scale, distributed innovation without compromising intellectual property.
Generative AI can now be applied to sensitive, distributed hardware design datasets, accelerating electronic design automation and potentially lowering barriers to entry for advanced chip design.
- · Semiconductor companies (smaller players)
- · Electronic Design Automation (EDA) software providers
- · Generative AI researchers
- · Hardware startups
- · Companies reliant on siloed hardware IP for competitive advantage
- · Traditional, manual chip design workflows
- · Centralized AI training models
AnalogFed enables privacy-preserving generative AI for analog circuit design by leveraging federated learning.
This framework could lead to faster, more efficient, and more diverse analog circuit development, democratizing access to advanced hardware design capabilities.
Accelerated innovation in analog circuits could indirectly boost performance and efficiency across numerous compute-intensive sectors, from AI accelerators to telecommunications infrastructure.
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Read at arXiv cs.AI