AI-Assisted Copilot Automation for Reliable FPGA Verification at Hyperscale

Cloud-scale, Artificial Intelligence (AI)-centric deployments are increasingly accelerated and made flexible through Field-Programmable Gate Arrays (FPGAs). Traditional simulation-only verification has often been shown to miss system-level risks that surface late during hardware bring-up. A hybrid verification methodology has therefore been developed in which AI-driven automation is orchestrated with formal connectivity checks to ensure reliable end-to-end signal connectivity and robust reset behavior across heterogeneous FPGA Stock Keeping Unit (SKU) variants. Structured prompts are employed to guide Copilot in generating Python scripts that traverse - Register Transfer Level (RTL) designs, extract signal mappings, and auto-generate timing-aware connectivity assertions in Comma-Separated Values (CSV) format. Precise, context-rich prompts specifying signal roles, hierarchy depth, and reset domains are observed to yield consistent results, whereas generic prompts fail to capture architectural nuances. Reset validation strategies covering Function Level Resets (FLRs), Memory-Mapped Input Output (MMIO) handlers, and firmware-driven resets have been applied in FPGA contexts, while the underlying connectivity assertion framework has been shown to be portable to Application-Specific Integrated Circuit (ASIC) flows with minimal adaptation. Over nine months, the methodology was deployed on multiple hyperscale FPGA platforms, where critical bugs missed by weeks of simulation regressions were surfaced. Integration with commercial formal tools was achieved, scalability to new FPGA variants was demonstrated, and lessons in prompt engineering and cross-domain verification were documented.