PEng4NN: An Accurate Performance Estimation Engine for Efficient
Automated Neural Network Architecture Search
- URL: http://arxiv.org/abs/2101.04185v1
- Date: Mon, 11 Jan 2021 20:49:55 GMT
- Title: PEng4NN: An Accurate Performance Estimation Engine for Efficient
Automated Neural Network Architecture Search
- Authors: Ariel Keller Rorabaugh (1), Silvina Ca\'ino-Lores (1), Michael R.
Wyatt II (1), Travis Johnston (2), Michela Taufer (1) ((1) University of
Tennessee, Knoxville, USA, (2) Oak Ridge National Lab, Oak Ridge, USA)
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Neural network (NN) models are increasingly used in scientific simulations,
AI, and other high performance computing (HPC) fields to extract knowledge from
datasets. Each dataset requires tailored NN model architecture, but designing
structures by hand is a time-consuming and error-prone process. Neural
architecture search (NAS) automates the design of NN architectures. NAS
attempts to find well-performing NN models for specialized datsets, where
performance is measured by key metrics that capture the NN capabilities (e.g.,
accuracy of classification of samples in a dataset). Existing NAS methods are
resource intensive, especially when searching for highly accurate models for
larger and larger datasets.
To address this problem, we propose a performance estimation strategy that
reduces the resources for training NNs and increases NAS throughput without
jeopardizing accuracy. We implement our strategy via an engine called PEng4NN
that plugs into existing NAS methods; in doing so, PEng4NN predicts the final
accuracy of NNs early in the training process, informs the NAS of NN
performance, and thus enables the NAS to terminate training NNs early. We
assess our engine on three diverse datasets (i.e., CIFAR-100, Fashion MNIST,
and SVHN). By reducing the training epochs needed, our engine achieves
substantial throughput gain; on average, our engine saves $61\%$ to $82\%$ of
training epochs, increasing throughput by a factor of 2.5 to 5 compared to a
state-of-the-art NAS method. We achieve this gain without compromising
accuracy, as we demonstrate with two key outcomes. First, across all our tests,
between $74\%$ and $97\%$ of the ground truth best models lie in our set of
predicted best models. Second, the accuracy distributions of the ground truth
best models and our predicted best models are comparable, with the mean
accuracy values differing by at most .7 percentage points across all tests.
Related papers
- NAS-BNN: Neural Architecture Search for Binary Neural Networks [55.058512316210056]
We propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN.
Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M.
In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS dataset.
arXiv Detail & Related papers (2024-08-28T02:17:58Z) - A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism [58.855741970337675]
Neural architecture search (NAS) enables re-searchers to automatically explore vast search spaces and find efficient neural networks.
NAS suffers from a key bottleneck, i.e., numerous architectures need to be evaluated during the search process.
We propose the SMEM-NAS, a pairwise com-parison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism.
arXiv Detail & Related papers (2024-07-22T12:46:22Z) - 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) - Do Not Train It: A Linear Neural Architecture Search of Graph Neural
Networks [15.823247346294089]
We develop a novel NAS-GNNs method, namely neural architecture coding (NAC)
Our approach leads to state-of-the-art performance, which is up to $200times$ faster and $18.8%$ more accurate than the strong baselines.
arXiv Detail & Related papers (2023-05-23T13:44:04Z) - Accelerating Multi-Objective Neural Architecture Search by Random-Weight
Evaluation [24.44521525130034]
We introduce a new performance estimation metric named Random-Weight Evaluation (RWE) to quantify the quality of CNNs.
RWE only trains its last layer and leaves the remainders with randomly weights, which results in a single network evaluation in seconds.
Our proposed method obtains a set of efficient models with state-of-the-art performance in two real-world search spaces.
arXiv Detail & Related papers (2021-10-08T06:35:20Z) - ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked
Models [56.21470608621633]
We propose a time estimation framework to decouple the architectural search from the target hardware.
The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation.
We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
arXiv Detail & Related papers (2021-05-07T11:39:05Z) - Search to aggregate neighborhood for graph neural network [47.47628113034479]
We propose a framework, which tries to Search to Aggregate NEighborhood (SANE) to automatically design data-specific GNN architectures.
By designing a novel and expressive search space, we propose a differentiable search algorithm, which is more efficient than previous reinforcement learning based methods.
arXiv Detail & Related papers (2021-04-14T03:15:19Z) - AttentiveNAS: Improving Neural Architecture Search via Attentive
Sampling [39.58754758581108]
Two-stage Neural Architecture Search (NAS) achieves remarkable accuracy and efficiency.
Two-stage NAS requires sampling from the search space during training, which directly impacts the accuracy of the final searched models.
We propose AttentiveNAS that focuses on improving the sampling strategy to achieve better performance Pareto.
Our discovered model family, AttentiveNAS models, achieves top-1 accuracy from 77.3% to 80.7% on ImageNet, and outperforms SOTA models, including BigNAS and Once-for-All networks.
arXiv Detail & Related papers (2020-11-18T00:15:23Z) - PV-NAS: Practical Neural Architecture Search for Video Recognition [83.77236063613579]
Deep neural networks for video tasks is highly customized and the design of such networks requires domain experts and costly trial and error tests.
Recent advance in network architecture search has boosted the image recognition performance in a large margin.
In this study, we propose a practical solution, namely Practical Video Neural Architecture Search (PV-NAS)
arXiv Detail & Related papers (2020-11-02T08:50:23Z) - Direct Federated Neural Architecture Search [0.0]
We present an effective approach for direct federated NAS which is hardware agnostic, computationally lightweight, and a one-stage method to search for ready-to-deploy neural network models.
Our results show an order of magnitude reduction in resource consumption while edging out prior art in accuracy.
arXiv Detail & Related papers (2020-10-13T08:11:35Z) - BRP-NAS: Prediction-based NAS using GCNs [21.765796576990137]
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.
arXiv Detail & Related papers (2020-07-16T21:58:43Z)
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.