Sensor Pre-Acquisition Neural Architecture Search

Optimal neural architectures depend on input data acquisition, yet neural architecture search (NAS) typically fixes acquisition, excluding alternative acquisition–architecture pairs. Changing pre-acquisition parameters changes observations, preventing direct backpropagation through acquisition choices. Black-box joint search avoids this requirement but incurs substantial cost from training candidate pairs separately. We propose SPANAS, a NAS framework exploiting ordinal acquisition parameters to jointly search configurations and architectures without a differentiable acquisition model. A Rao–Blackwellized loss contrast between adjacent measured levels yields an unbiased within-interval selector gradient under fixed shared weights and generator parameters, without interpolating observations. A structured configuration representation conditions a hypernetwork over a weight-sharing supernetwork. Refinement and retraining verification produce an empirical accuracy–storage front. We evaluate SPANAS on FMCW-radar marshalling-signal recognition and a public wearable inertial benchmark. In both domains, SPANAS achieves higher mean hypervolume and mean best recognition performance than the evaluated black-box campaigns, raising hypervolume by 11% on radar and 7% on IMU over the strongest campaign. Its front also dominates every evaluated differentiable NAS result in both domains. On IMU, SPANAS achieves a higher macro-F1 than the strongest domain-specific architecture at the placement recommended by prior work with 5.7× less storage.