FNAS: Uncertainty-Aware Fast Neural Architecture Search

• URL: http://arxiv.org/abs/2105.11694v3
• Date: Thu, 27 May 2021 07:53:59 GMT
• Title: FNAS: Uncertainty-Aware Fast Neural Architecture Search
• Authors: Jihao Liu and Ming Zhang and Yangting Sun and Boxiao Liu and Guanglu Song and Yu Liu and Hongsheng Li
• Abstract summary: Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources. We propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS. Experiments on the Mobile Neural Architecture Search (MNAS) search space show the proposed Fast Neural Architecture Search (FNAS) accelerates standard RL-based NAS process by 10x.
• Score: 54.49650267859032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -- exhaustive training for each sampled generation on proxy tasks. In this paper, we propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS. It is motivated by the interesting observation that both the architecture and the parameter knowledge can be transferred between different experiments and even different tasks. We first introduce an uncertainty-aware critic (value function) in Proximal Policy Optimization (PPO) to utilize the architecture knowledge in previous experiments, which stabilizes the training process and reduces the searching time by 4 times. Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times. It is the first to introduce block-level weight sharing in RLbased NAS. The block similarity function guarantees a 100% hitting ratio with strict fairness. Besides, we show that a simply designed off-policy correction factor used in "replay buffer" in RL optimization can further reduce half of the searching time. Experiments on the Mobile Neural Architecture Search (MNAS) search space show the proposed Fast Neural Architecture Search (FNAS) accelerates standard RL-based NAS process by ~10x (e.g. ~256 2x2 TPUv2 x days / 20,000 GPU x hour -> 2,000 GPU x hour for MNAS), and guarantees better performance on various vision tasks.

Related papers

• Search-time Efficient Device Constraints-Aware Neural Architecture Search [6.527454079441765]
  Deep learning techniques like computer vision and natural language processing can be computationally expensive and memory-intensive. We automate the construction of task-specific deep learning architectures optimized for device constraints through Neural Architecture Search (NAS) We present DCA-NAS, a principled method of fast neural network architecture search that incorporates edge-device constraints.
  arXiv  Detail & Related papers  (2023-07-10T09:52:28Z)
• DCP-NAS: Discrepant Child-Parent Neural Architecture Search for 1-bit CNNs [53.82853297675979]
  1-bit convolutional neural networks (CNNs) with binary weights and activations show their potential for resource-limited embedded devices. One natural approach is to use 1-bit CNNs to reduce the computation and memory cost of NAS. We introduce Discrepant Child-Parent Neural Architecture Search (DCP-NAS) to efficiently search 1-bit CNNs.
  arXiv  Detail & Related papers  (2023-06-27T11:28:29Z)
• POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique [7.497722345725035]
  This paper proposes a new version of the Pareto-optimal Progressive Neural Architecture Search, called POPNASv2. Our approach enhances its first version and improves its performance. Our efforts allow POPNASv2 to achieve PNAS-like performance with an average 4x factor search time speed-up.
  arXiv  Detail & Related papers  (2022-10-06T14:51:54Z)
• $\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search [85.84110365657455]
  We propose a simple-but-efficient regularization method, termed as Beta-Decay, to regularize the DARTS-based NAS searching process. Experimental results on NAS-Bench-201 show that our proposed method can help to stabilize the searching process and makes the searched network more transferable across different datasets.
  arXiv  Detail & Related papers  (2022-03-03T11:47:14Z)
• 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)
• L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning [23.25155249879658]
  Differentiable architecture search (NAS) achieved remarkable results in deep neural network design. We show that L$2$ achieves state-of-theart results on DART201 benchmark as well as NASS and Once-for-All search policies.
  arXiv  Detail & Related papers  (2021-09-25T19:26:30Z)
• Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration [68.6505473346005]
  We propose a memory-efficient hierarchical NAS HiNAS (HiNAS) for image denoising and image super-resolution tasks. With a single GTX1080Ti GPU, it takes only about 1 hour for searching for denoising network on BSD 500 and 3.5 hours for searching for the super-resolution structure on DIV2K.
  arXiv  Detail & Related papers  (2020-12-24T12:06:17Z)
• 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)
• 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.