TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework

TL;DR

TND-NAS introduces a high-efficiency differentiable NAS framework capable of optimizing non-differentiable objectives like energy and resource constraints, enabling multi-objective architecture search with reduced computational cost.

Contribution

The paper proposes TND-NAS, a novel method that integrates non-differentiable metrics into the differentiable NAS framework through discrete architecture parameter optimization and progressive search space shrinking.

Findings

Achieves high-performance architectures on CIFAR datasets.

Reduces search time to 1.3 GPU-days, significantly faster than existing methods.

Effectively handles multi-objective optimization including parameters and accuracy.

Abstract

Differentiable architecture search has gradually become the mainstream research topic in the field of Neural Architecture Search (NAS) for its high efficiency compared with the early NAS methods. Recent differentiable NAS also aims at further improving the search performance and reducing the GPU-memory consumption. However, these methods are no longer naturally capable of tackling the non-differentiable objectives, e.g., energy, resource-constrained efficiency, and other metrics, let alone the multi-objective search demands. Researches in the multi-objective NAS field target this but requires vast computational resources cause of the sole optimization of each candidate architecture. In light of this discrepancy, we propose the TND-NAS, which is with the merits of the high efficiency in differentiable NAS framework and the compatibility among non-differentiable metrics in Multi-objective NAS. Under the differentiable NAS framework, with the continuous relaxation of the search space, TND-NAS has the architecture parameters been optimized in discrete space, while resorting to the progressive search space shrinking by architecture parameters. Our representative experiment takes two objectives (Parameters, Accuracy) as an example, we achieve a series of high-performance compact architectures on CIFAR10 (1.09M/3.3%, 2.4M/2.95%, 9.57M/2.54%) and CIFAR100 (2.46M/18.3%, 5.46/16.73%, 12.88/15.20%) datasets. Favorably, compared with other multi-objective NAS methods, TND-NAS is less time-consuming (1.3 GPU-days on NVIDIA 1080Ti, 1/6 of that in NSGA-Net), and can be conveniently adapted to real-world NAS scenarios (resource-constrained, platform-specialized).