CircuitOps: An ML Infrastructure Enabling Generative AI for VLSI Circuit Optimization

An innovative ML infrastructure named CircuitOps is developed to streamline dataset generation and model inference for various generative AI (GAI)-based circuit optimization tasks. Addressing the challenges of the absence of a shared Intermediate Representation (IR), steep EDA learning curves, and AI-unfriendly data structures, we propose solutions that empower efficient data handling. Our contributions encompass the following: (1) labeled property graphs (LPGs) as IR for flexible netlist representation and efficient parallel processing; (2) tools-agnostic IR generation from standard EDA files; (3) customizable dataset generation facilitated through AI-friendly LPGs; (4) gRPC-based inference deployment. Compared with using Tcl interfaces of EDA design tools, CircuitOps achieves a significant 99x dataset generation speedup and 75K nets per second transfer throughput, validating its effectiveness in optimizing GAI tasks.


Geraldo Pradipta (NVIDIA)
Vidya A. Chhabria (Arizona State University)

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