DNASNet: Decoupled Neural Architecture Search Driven by Task-Conditioned Spike Timing

Spiking neural networks (SNNs) promise energy-efficient computation, but differentiable search over temporally unrolled SNNs is costly. DNASNet decouples weight-sharing NAS: spatio-temporal backpropagation (STBP) learns shared weights, while an STDP-inspired rule updates architectures from task-induced spike timing without architecture-level backpropagation. With network state and timing evidence fixed, the STDP utility is the gradient of an explicit concave temporal-balance potential, and the normalized update equals entropy mirror ascent on node-wise probability simplices. DNASNet produces stable architectures across random runs, achieves near-state-of-the-art performance on CIFAR-10, CIFAR-100, and DVS-Gesture, and yields competitive results on DVS-CIFAR-10 and ImageNet1K. Compared with bilevel optimization, it reduces peak search memory by up to 22% and search time by up to 51%. Ablations further confirm the importance of timing information for architectural consistency and retraining performance.