OrdinalForge: A Score-Induced Rank Representation for Scalable Training-Free DNN–Hardware Co-Optimization

As modern edge AI applications continue to grow in computational complexity, co-optimizing Deep Neural Networks (DNNs) and their underlying hardware becomes increasingly critical. Existing Neural Architecture Search (NAS) approaches face two major challenges: the high dimensionality of the joint DNN–hardware representation and the substantial costs of evaluating DNNs through training. Particularly, while training-free NAS can reduce evaluation costs, using the proxy score as the DNN objective alone does not reduce the dimensionality of the surrogate input. To address this limitation, we introduce OrdinalForge, a training-free DNN–hardware co-optimization framework using a score-induced rank formulation for surrogate-based optimization. By sorting the proxy scores and assigning each DNN candidate a scalar rank, OrdinalForge replaces the original multidimensional DNN candidates supplied to the surrogate with a one-dimensional ordered input, allowing the surrogate to model the proxy score as a monotonic function of rank. Experiments show that OrdinalForge improves hypervolume by up to 13.7× and reduces search time by up to 57.8× compared to training-based baselines in image classification and semantic segmentation workloads. It also achieves the highest mean final cumulative hypervolume and better Pareto-optimal solutions among the evaluated methods in image classification, semantic segmentation, and RNN-based language modeling tasks. In the GPT-2-based language processing workload, the score-induced rank formulation improves the mean final cumulative hypervolume compared to its corresponding original-encoding baselines, validating that the proposed approach is effective, generalizable, and scalable.