Surrogate-Assisted Framework for SI-Compliant Interconnect Design Optimization Using the Earth Mover's Distance
arXiv:2606.15234v1 Announce Type: cross Abstract: This work presents a deterministic, machine-assisted framework for SI-compliant PCB design based on the Earth Mover's Distance (EMD). In contrast to conventional surrogate-based optimization methods that rely on iterative black-box search procedures, the proposed approach follows an interpretable, sequential evaluation strategy. Neural surrogate models are first used to efficiently predict waveform describing features from topology-dependent design parameters. A decision tree then acts as a physically motivated quality gate that identifies SI-c
The rapid advancement of AI and increasing complexity of PCB design necessitate more efficient and interpretable optimization methods to accelerate hardware development for advanced computing. This specific research presents a novel, deterministic approach using AI for this critical task, published as AI hardware design becomes a bottleneck.
Improved SI-compliant PCB design is crucial for the reliability and performance of high-speed electronics, directly impacting the capabilities and efficiency of AI acceleration hardware and large-scale computing infrastructure. This deterministic approach reduces design cycles and increases yield, which is vital for scaling AI compute.
The conventional iterative, black-box optimization process for PCB design can now be augmented or potentially replaced by a more interpretable, sequential AI-driven strategy using neural surrogates and decision trees. This promises faster, more reliable, and more resource-efficient hardware development.
- · AI hardware manufacturers
- · Semiconductor industry
- · High-performance computing sector
- · EDA software providers
- · Traditional manual PCB design processes
- · Companies with less advanced design automation
- · Iterative black-box optimization methods
Faster and more efficient development cycles for advanced AI chips and systems will be realized.
The cost of developing high-performance computing infrastructure may decrease, accelerating the widespread adoption of AI technologies.
Increased accessibility to advanced AI hardware could democratize AI development, fostering innovation across a broader range of enterprises and potentially leading to new industry leaders.
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Read at arXiv cs.LG