CATOS: Compute–Accuracy Tradeoff Scalarization for Multiobjective Optimization in Neural architecture Search

Neural architecture search (NAS) increasingly targets deployment on resource-constrained hardware such as mobile devices or edge accelerators, where accuracy must be weighed against efficiency metrics such as latency or FLOPs, motivating multiobjective optimization (MOO). Existing MOO methods for NAS rest on Pareto dominance, which induces only a partial ordering: whenever one architecture is more accurate and another more efficient, the two are incomparable, preventing a consistent ranking of tradeoffs across the search space. As a result, existing multiobjective NAS methods rely on heuristic ranking strategies built on Pareto-based indicators, which provide only indirect signals of tradeoffs and are sensitive to the sampled set of architectures. To address these limitations, we introduce CATOS (Compute-Accuracy Tradeoff Scalarization), a scalarization function built on novel mathematical constructs that ranks architectures under bi-objective tradeoffs and remains effective in low-sample regimes by leveraging a compute-accuracy scaling prior. Integrated into an evolutionary algorithm (CATOS-EA), our method outperforms state-of-the-art MOO algorithms as well as constrained approaches on NATS-Bench in nearly all settings. Notably, the constructs underlying CATOS transfer unchanged to the markedly different NLP benchmark HW-GPT-Bench, where the augmented CATOS outperforms the strongest baseline.