Experiential neural architecture selection: dynamic cross-layer memory for real-time inference optimization

Neural networks suffer from operational amnesia: they process each input as if it were the first time, without remembering which neuron combinations proved effective in similar contexts. We introduce ExNAS ( Experiential Neural Architecture Selection ), a system that performs real-time, neuron-granular architectural adaptation during the same inference by leveraging a distributed experiential memory. ExNAS records layer-wise neural fingerprints and lightweight contextual metadata and then performs transversal selection across non-consecutive layers under explicit per-layer and global budgets. On a CPU proof-of-concept using a small CNN (2×Conv+FC), ExNAS delivers measurable time reductions (≈3.7–7.9%) and throughput gains (≈3.8–8.5%) at low active fractions (≈4.7–10.9%), without retraining. We detail the design, provide formal definitions, and discuss sensitivity to budgets and a negative case where heavier adaptation adds overhead. These results substantiate experience-guided, neuron-level conditional computation as a practical tool for real-time inference.