For NAS-Bench-201, INT4 post-training quantization fully reorganizes the Pareto front, yet an FP32 zero-shot surrogate still achieves higher Pareto-space coverage than an INT4-trained surrogate.
”Multi-objective hardware-aware neural ar- chitecture search with Pareto rank-preserving surrogate models.” ACM Transactions on Architecture and Code Optimization 20.2 (2023): 1-21
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NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
For NAS-Bench-201, INT4 post-training quantization fully reorganizes the Pareto front, yet an FP32 zero-shot surrogate still achieves higher Pareto-space coverage than an INT4-trained surrogate.