SECDA-DSE puts an LLM inside the design space exploration loop for FPGA-based AI accelerators. What used to require a domain expert spending weeks iterating through architectural parameters, dataflow strategies, and memory hierarchies is now a guided search driven by retrieval-augmented generation and chain-of-thought reasoning against simulation results.
The underlying SECDA framework already enabled rapid hardware-software co-design through SystemC simulation and FPGA execution. The bottleneck SECDA could not remove was the decision step: which configuration to try next when the search space is exponential and intuition-dependent. SECDA-DSE adds an LLM Stack that reads simulation results, reasons about what to try, generates new candidate configurations, and feeds outcomes back into a reinforced fine-tuning loop. Teams already using SECDA get LLM-guided DSE as an upgrade, not a rewrite. The tool runs on top of existing SECDA simulation infrastructure, so teams already using SECDA do not need to rebuild to try it.
The challenge to the "senior engineer who knows the design space" model of FPGA accelerator development is now formal and citable. If LLM-driven DSE navigates the configuration space in a fraction of the calendar time, the constraint removed is expert intuition as a rate limiter. Teams evaluating LLM inference accelerators on FPGAs could cut prototype-to-validated-configuration cycles from weeks to days. The interesting question is whether DSE automation shows up in commercial FPGA tools (Vitis, Quartus) within 18 months or whether the open-source toolchain gets there first.