operator-development

To ensure cmodel and RTL behavioral consistency for LPU/NPU operator development, follow a standardized verification ladder from pymodel compilation to cmodel replay and UVM simulation. This workflow validates tile/buffer planning and synchronization controls to quickly locate GM mismatch and timing issues.

You need to insert set_flag and wait_flag synchronization mechanisms in AI accelerator instructions whenever managing cross-execution-unit data visibility and concurrent scheduling. Proper insertion ensures safe buffer reuse and prevents memory conflicts during complex operators like prefill/decode attention.

To debug GM mismatch issues during cmodel replay for prefill attention operators, trace the instruction flow synchronization logic and verify fence insertions. Validating the task bin generation against the pymodel helps isolate incorrect tile/buffer planning or missing synchronization controls.

The best way to plan tile and buffer scheduling for complex NPU operators like KV cache and GQA is to use a standardized workflow from compute graph confirmation to task bin generation. This ensures correct set-wait synchronization logic and safe cross-execution-unit concurrent dispatch.

Your cmodel replay fails with buffer reuse conflicts in decode attention workloads due to missing or incorrect fence insertions and synchronization mechanisms. Properly inserting set_flag and wait_flag ensures safe data visibility across execution units and resolves these memory hazards.

Yes, you can use this operator development workflow to validate synchronization for both prefill and decode attention. It supports complex operator instruction flows by verifying set-wait logic and generating task bins for rigorous cmodel replay and UVM simulation.