Equation 26 · Shrink It, Train It Small, or Search for It: The Main Strategies for Small Models, Compared
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The field is not fully settled on how much of the search should be automated in the first place, and one of the strongest edge-architecture results says so in its own account of its method. Howard and colleagues built MobileNetV3 by explicitly “combining hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm” with new, manually designed architecture components [ 9 ] — a leading result that does not claim pure automated search was sufficient on its own. That is worth stating as a genuine, documented tension rather than resolving it: platform-aware search measurably beats hand design and beats proxy-driven search, and the strongest published edge architectures…
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The field is not fully settled on how much of the search should be automated in the first place, and one of the strongest edge-architecture results says so in its own account of its method. Howard and colleagues built MobileNetV3 by explicitly “combining hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm” with new, manually designed architecture components [ 9 ] — a leading result that does not claim pure automated search was sufficient on its own. That is worth stating as a genuine, documented tension rather than resolving it: platform-aware search measurably beats hand design and beats proxy-driven search, and the strongest published edge architectures still keep a human in the loop rather than treating the search space as the final word. Beyond that tension, two limitations apply regardless of which position one takes: the search space is itself a human-authored boundary on what can be discovered, and a latency- or energy-aware objective measured on one reference device is not guaranteed to hold on a different chip generation without re-measurement.
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