AI RESEARCH
Surrogate Neural Architecture Codesign Package (SNAC-Pack)
arXiv CS.AI
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ArXi:2605.16138v1 Announce Type: cross Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency.