Editing Neural Architecture Search (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a hardware-aware NAS pipeline:
# Profile all candidate operations on target device with lookup table (latency per op).
# Define search space as cell structure with K=8–14 candidate operations per edge.
# Run one-shot supernet training with uniform operation sampling.
# Evaluate sub-architectures by sampling 1000 random architectures, estimating performance using inherited supernet weights.
# Pareto-optimal selection: identify architectures on the accuracy-latency Pareto frontier.
# Retrain top-3 Pareto-optimal architectures from scratch to verify supernet rankings transfer.

[[Category:Artificial Intelligence]]
[[Category:Deep Learning]]
[[Category:AutoML]]
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