Agentic Optimization of Multi-FPGA Netlist Partitioning and Compiler Parameter Search For Enterprise Pre-Silicon Prototyping: A Feedback-Driven Framework For Hyperscale Soc Bring-Up Acceleration

Abstract

Pre-silicon validation of ultra-large system-on-chip designs represents one of the most infrastructure-intensive challenges in modern semiconductor development, where the gap between device complexity and available prototyping capacity continues to widen. Conventional multi-FPGA partitioning workflows rely on static heuristics and manual compiler tuning, yielding sub-optimal resource utilization and prohibitively long compilation cycles that delay validation schedules across enterprise design programs. This article introduces AiProto, an agentic artificial intelligence framework that transforms the partitioning and compiler optimization workflow through coordinated autonomous agent execution. AiProto couples a hypergraph-based netlist partitioner with a reinforcement-learning-driven compiler optimizer and a context-aware log reasoning module, enabling iterative, self-correcting optimization across multi-FPGA arrays without manual intervention. A confidence-gated execution model governs agent actions, ensuring that uncertain decisions are surfaced for review rather than propagated through the compilation pipeline. Evaluated on three production-class enterprise SoC designs with distinct prototyping bottlenecks, AiProto demonstrates substantial improvements in FPGA resource utilization, reduction in compilation iterations required to reach timing closure, and measurable compression of end-to-end validation cycle time. The framework integrates with existing electronic design automation toolchains through a Model Context Protocol interface, requiring no redesign of established validation infrastructure. These results position agentic AI as a viable and immediately deployable strategy for closing the prototyping capacity gap in next-generation semiconductor development programs.