What does this equation mean?

The first response changes what g(a) measures. Tan and colleagues built MnasNet’s rationale explicitly around the observation that operation counts are a poor stand-in for real efficiency, so the search “directly measures real-world inference latency by executing the model on mobile phones,” and reported an architecture 1.8x faster than MobileNetV2 at 0.5% higher accuracy, and 2.3x faster than NASNet at 1.2% higher accuracy [ 8 ] . That is a harder objective to satisfy than a proxy count, because it cannot be gamed by an architecture that is cheap on paper and slow in practice. The second response changes how often the expensive part of the search has to be paid at all. Cai and colleagues named the underlying problem directly — that manually designing or running NAS “for each case” of a target device “is computationally prohibitive” — and instead trained one large “supernet” a single time, from which a specialized sub-network for a given device could be extracted “without additional training” [ 10 ] . That converts a cost that would otherwise recur for every device generation into a cost paid once, followed by cheap selection.