What is SNAC-Pack and what problem does it solve?

SNAC-Pack is an open-source AutoML framework that bridges the gap between neural architecture search (NAS) and FPGA deployment. It uses hardware proxy models and multi-objective optimization to generate FPGA-optimized architectures end-to-end.

Why does SNAC-Pack matter for hardware deployment?

Traditional NAS methods ignore FPGA constraints like LUTs, DSPs, and BRAMs, producing models that cannot be efficiently implemented. SNAC-Pack incorporates hardware awareness throughout search, cutting quantum task design time from months to hours.

What should we watch for with SNAC-Pack going forward?

The framework could extend to ASIC and TPU platforms, support multimodal model co-design, and inspire more cross-disciplinary AI-assisted design applications in scientific computing and quantum research.

SNAC-Pack：面向FPGA部署之多目標神經架構與程式碼協同設計自動化框架

傳統神經架構搜索（NAS）在FPGA部署中常因忽略硬體多維約束（LUT、DSP、BRAM等）而導致搜索出的模型難以高效落地。SNAC-Pack透過結合Optuna與NSGA-II進行多目標全局搜索，並利用硬體代理模型快速估算資源與延遲，大幅降低綜合成本。隨後框架進入局部搜索階段，融合量化感知訓練與迭代幅度剪枝進行模型壓縮，最終透過hls4ml生成可部署的FPGA韌體。在LHC噴注分類和超導量子比特讀出任務上的實驗表明，該框架不僅能發現匹配或超越基線性能的小型架構，還可顯著降低FPGA資源佔用，並將量子任務設計週期從數月縮短至數小時。