BRP-NAS: Prediction-based NAS using GCNs

• URL: http://arxiv.org/abs/2007.08668v4
• Date: Tue, 19 Jan 2021 17:29:16 GMT
• Title: BRP-NAS: Prediction-based NAS using GCNs
• Authors: {\L}ukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah, Royson Lee, Hyeji Kim, Nicholas D. Lane
• Abstract summary: BRP-NAS is an efficient hardware-aware NAS enabled by an accurate performance predictor-based on graph convolutional network (GCN) We show that our proposed method outperforms all prior methods on NAS-Bench-101 and NAS-Bench-201. We also release LatBench -- a latency dataset of NAS-Bench-201 models running on a broad range of devices.
• Score: 21.765796576990137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural architecture search (NAS) enables researchers to automatically explore broad design spaces in order to improve efficiency of neural networks. This efficiency is especially important in the case of on-device deployment, where improvements in accuracy should be balanced out with computational demands of a model. In practice, performance metrics of model are computationally expensive to obtain. Previous work uses a proxy (e.g., number of operations) or a layer-wise measurement of neural network layers to estimate end-to-end hardware performance but the imprecise prediction diminishes the quality of NAS. To address this problem, we propose BRP-NAS, an efficient hardware-aware NAS enabled by an accurate performance predictor-based on graph convolutional network (GCN). What is more, we investigate prediction quality on different metrics and show that sample efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy. We show that our proposed method outperforms all prior methods on NAS-Bench-101 and NAS-Bench-201, and that our predictor can consistently learn to extract useful features from the DARTS search space, improving upon the second-order baseline. Finally, to raise awareness of the fact that accurate latency estimation is not a trivial task, we release LatBench -- a latency dataset of NAS-Bench-201 models running on a broad range of devices.

Related papers

• Multi-Predict: Few Shot Predictors For Efficient Neural Architecture Search [10.538869116366415]
  We introduce a novel search-space independent NN encoding based on zero-cost proxies that achieves sample-efficient prediction on multiple tasks and NAS search spaces. Our NN encoding enables multi-search-space transfer of latency predictors from NASBench-201 to FBNet in under 85 HW measurements.
  arXiv  Detail & Related papers  (2023-06-04T20:22:14Z)
• DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models [56.584561770857306]
  We propose a novel conditional Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG. Specifically, we consider the neural architectures as directed graphs and propose a graph diffusion model for generating them. We validate the effectiveness of DiffusionNAG through extensive experiments in two predictor-based NAS scenarios: Transferable NAS and Bayesian Optimization (BO)-based NAS. When integrated into a BO-based algorithm, DiffusionNAG outperforms existing BO-based NAS approaches, particularly in the large MobileNetV3 search space on the ImageNet 1K dataset.
  arXiv  Detail & Related papers  (2023-05-26T13:58:18Z)
• Generalization Properties of NAS under Activation and Skip Connection Search [66.8386847112332]
  We study the generalization properties of Neural Architecture Search (NAS) under a unifying framework. We derive the lower (and upper) bounds of the minimum eigenvalue of the Neural Tangent Kernel (NTK) under the (in)finite-width regime. We show how the derived results can guide NAS to select the top-performing architectures, even in the case without training.
  arXiv  Detail & Related papers  (2022-09-15T12:11:41Z)
• 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)
• Generic Neural Architecture Search via Regression [27.78105839644199]
  We propose a novel and generic neural architecture search (NAS) framework, termed Generic NAS (GenNAS) GenNAS does not use task-specific labels but instead adopts textitregression on a set of manually designed synthetic signal bases for architecture evaluation. We then propose an automatic task search to optimize the combination of synthetic signals using limited downstream-task-specific labels.
  arXiv  Detail & Related papers  (2021-08-04T08:21:12Z)
• PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search [0.0]
  Neural network (NN) models are increasingly used in scientific simulations, AI, and other high performance computing fields. NAS attempts to find well-performing NN models for specialized datsets, where performance is measured by key metrics that capture the NN capabilities. We propose a performance estimation strategy that reduces the resources for training NNs and increases NAS throughput without jeopardizing accuracy.
  arXiv  Detail & Related papers  (2021-01-11T20:49:55Z)
• MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
  We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS) We employ a one-shot architecture search approach in order to obtain a reduced search cost. We achieve state-of-the-art results in terms of accuracy-speed trade-off.
  arXiv  Detail & Related papers  (2020-09-29T11:56:01Z)
• 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)
• Accuracy Prediction with Non-neural Model for Neural Architecture Search [185.0651567642238]
  We study an alternative approach which uses non-neural model for accuracy prediction. We leverage gradient boosting decision tree (GBDT) as the predictor for Neural architecture search (NAS) Experiments on NASBench-101 and ImageNet demonstrate the effectiveness of using GBDT as predictor for NAS.
  arXiv  Detail & Related papers  (2020-07-09T13:28:49Z)

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.