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

• URL: http://arxiv.org/abs/2111.03892v4
• Date: Sat, 1 Jul 2023 12:36:56 GMT
• Title: TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework
• Authors: Bo Lyu, Shiping Wen
• Abstract summary: Differentiable architecture search has gradually become the mainstream research topic in the field of Neural Architecture Search (NAS) Recent differentiable NAS also aims at further improving the search performance and reducing the GPU-memory consumption. 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.
• Score: 6.895590095853327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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).

Related papers

• Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge [21.72253397805102]
  This work focuses in particular on Temporal Convolutional Networks (TCNs), a convolutional model for time-series processing. We propose the first NAS tool that explicitly targets the optimization of the most peculiar architectural parameters of TCNs. We test the proposed NAS on four real-world, edge-relevant tasks, involving audio and bio-signals.
  arXiv  Detail & Related papers  (2023-01-24T19:47:40Z)
• U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search [50.33956216274694]
  optimizing resource utilization in target platforms is key to achieving high performance during DNN inference. We propose a novel hardware-aware NAS framework that does not only optimize for task accuracy and inference latency, but also for resource utilization. We achieve 2.8 - 4x speedup for DNN inference compared to prior hardware-aware NAS methods.
  arXiv  Detail & Related papers  (2022-03-23T13:44:15Z)
• BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule [95.56873042777316]
  Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost. This paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability.
  arXiv  Detail & Related papers  (2021-11-25T18:13:42Z)
• NAS-Bench-360: Benchmarking Diverse Tasks for Neural Architecture Search [18.9676056830197]
  Most existing neural architecture search (NAS) benchmarks and algorithms prioritize performance on well-studied tasks. We present NAS-Bench-360, a benchmark suite for evaluating state-of-the-art NAS methods for convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2021-10-12T01:13:18Z)
• AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment [23.988393741948485]
  We propose a novel search strategy for one-shot and sparse propagation NAS, namely AdvantageNAS. AdvantageNAS is a gradient-based approach that improves the search efficiency by introducing credit assignment in gradient estimation for architecture updates. Experiments on the NAS-Bench-201 and PTB dataset show that AdvantageNAS discovers an architecture with higher performance under a limited time budget.
  arXiv  Detail & Related papers  (2020-12-11T05:45:03Z)
• Binarized Neural Architecture Search for Efficient Object Recognition [120.23378346337311]
  Binarized neural architecture search (BNAS) produces extremely compressed models to reduce huge computational cost on embedded devices for edge computing. An accuracy of $96.53%$ vs. $97.22%$ is achieved on the CIFAR-10 dataset, but with a significantly compressed model, and a $40%$ faster search than the state-of-the-art PC-DARTS.
  arXiv  Detail & Related papers  (2020-09-08T15:51:23Z)
• DSNAS: Direct Neural Architecture Search without Parameter Retraining [112.02966105995641]
  We propose a new problem definition for NAS, task-specific end-to-end, based on this observation. We propose DSNAS, an efficient differentiable NAS framework that simultaneously optimize architecture and parameters with a low-biased Monte Carlo estimate. DSNAS successfully discovers networks with comparable accuracy (74.4%) on ImageNet in 420 GPU hours, reducing the total time by more than 34%.
  arXiv  Detail & Related papers  (2020-02-21T04:41:47Z)
• NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search [42.82951139084501]
  One-shot neural architecture search (NAS) has played a crucial role in making NAS methods computationally feasible in practice. We introduce a general framework for one-shot NAS that can be instantiated to many recently-introduced variants and introduce a general benchmarking framework.
  arXiv  Detail & Related papers  (2020-01-28T15:50:22Z)
• NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search [55.12928953187342]
  We propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms.
  arXiv  Detail & Related papers  (2020-01-02T05:28:26Z)
• DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning [135.27931587381596]
  We propose an efficient and unified NAS framework termed DDPNAS via dynamic distribution pruning. In particular, we first sample architectures from a joint categorical distribution. Then the search space is dynamically pruned and its distribution is updated every few epochs. With the proposed efficient network generation method, we directly obtain the optimal neural architectures on given constraints.
  arXiv  Detail & Related papers  (2019-05-28T06:35:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.