Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

We address the challenging problem of efficient deep learning model deployment, where the goal is to design neural network architectures that can fit different hardware platform constraints.
Most of the traditional approaches either manually design or use Neural Architecture Search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.
Our key idea is to decouple model training from architecture search to save the cost.
To this end, we propose to train a once-for-all network (OFA) that supports diverse architectural settings (depth, width, kernel size, and resolution).
Given a deployment scenario, we can then quickly get a specialized sub-network by selecting from the OFA network without additional training.
To prevent interference between many sub-networks during training, we also propose a novel progressive shrinking algorithm, which can train a surprisingly large number of sub-networks ($> 10^{19}$) simultaneously.
Extensive experiments on various hardware platforms (CPU, GPU, mCPU, mGPU, FPGA accelerator) show that OFA consistently outperforms SOTA NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3) while reducing orders of magnitude GPU hours and $CO_2$ emission.
In particular, OFA achieves a new SOTA 80.0% ImageNet top1 accuracy under the mobile setting ($<$600M FLOPs).
Code and pre-trained models are released at https://github.com/mit-han-lab/once-for-all.
Deep Neural Networks (DNNs) deliver state-of-the-art accuracy in many machine learning applications.
However, the explosive growth in model size and computation cost gives rise to new challenges on how to efficiently deploy these deep learning models on diverse hardware platforms, since they have to meet different hardware efficiency constraints (e.g., latency, energy).
For instance, one mobile application on App Store has to support a diverse range of hardware devices, from a high-end Samsung Note10 with a dedicated neural network accelerator to a 5-year-old Samsung S5 with a much slower processor.
With different hardware resources (e.g., on-chip memory size, #arithmetic units), the optimal neural network architecture varies significantly.
Even running on the same hardware, under different battery conditions or workloads, the best model architecture also differs a lot.
Given different hardware platforms and efficiency constraints (defined as deployment scenarios), researchers either design compact models specialized for mobile (Howard et al., 2017; Sandler et al., 2018; or accelerate the existing models by compression (He et al., 2018) for efficient deployment.
However, designing specialized DNNs for every scenario is engineer-expensive and computationally expensive, either with human-based methods or NAS.
Since such methods need to repeat the network design process and retrain the designed network from scratch for each case.
Their total cost grows linearly as the number of deployment scenarios increases, which will result in excessive energy consumption and CO 2 emission (Strubell et al., 2019) .
It makes them unable to handle the vast amount of hardware devices (23.14 billion IoT devices till 2018 1 ) and highly dynamic deployment environments (different battery conditions, different latency requirements, etc.).
This paper introduces a new solution to tackle this challenge -designing a once-for-all network that can be directly deployed under diverse architectural configurations, amortizing the training cost.
The Figure 1: Left: a single once-for-all network is trained to support versatile architectural configurations including depth, width, kernel size, and resolution.
Given a deployment scenario, a specialized subnetwork is directly selected from the once-for-all network without training.
Middle: this approach reduces the cost of specialized deep learning deployment from O(N) to O(1).
Right: once-for-all network followed by model selection can derive many accuracy-latency trade-offs by training only once, compared to conventional methods that require repeated training.
inference is performed by selecting only part of the once-for-all network.
It flexibly supports different depths, widths, kernel sizes, and resolutions without retraining.
A simple example of Once for All (OFA) is illustrated in Figure 1 (left).
Specifically, we decouple the model training stage and the model specialization stage.
In the model training stage, we focus on improving the accuracy of all sub-networks that are derived by selecting different parts of the once-for-all network.
In the model specialization stage, we sample a subset of sub-networks to train an accuracy predictor and latency predictors.
Given the target hardware and constraint, a predictor-guided architecture search (Liu et al., 2018a ) is conducted to get a specialized sub-network, and the cost is negligible.
As such, we reduce the total cost of specialized neural network design from O(N) to O(1) (Figure 1 middle).
However, training the once-for-all network is a non-trivial task, since it requires joint optimization of the weights to maintain the accuracy of a large number of sub-networks (more than 10 19 in our experiments).
It is computationally prohibitive to enumerate all sub-networks to get the exact gradient in each update step, while randomly sampling a few sub-networks in each step will lead to significant accuracy drops.
The challenge is that different sub-networks are interfering with each other, making the training process of the whole once-for-all network inefficient.
To address this challenge, we propose a progressive shrinking algorithm for training the once-for-all network.
Instead of directly optimizing the once-for-all network from scratch, we propose to first train the largest neural network with maximum depth, width, and kernel size, then progressively fine-tune the once-for-all network to support smaller sub-networks that share weights with the larger ones.
As such, it provides better initialization by selecting the most important weights of larger sub-networks, and the opportunity to distill smaller sub-networks, which greatly improves the training efficiency.
We extensively evaluated the effectiveness of OFA on ImageNet with many hardware platforms (CPU, GPU, mCPU, mGPU, FPGA accelerator) and efficiency constraints.
Under all deployment scenarios, OFA consistently improves the ImageNet top1 accuracy by a significant margin compared to SOTA hardware-aware NAS methods while saving the GPU hours, dollars, and CO 2 emission by orders of magnitude.
On the ImageNet mobile setting, OFA achieves a new SOTA 80.0% top1 accuracy with 595M FLOPs.
To the best of our knowledge, this is the first time that the SOTA ImageNet top1 accuracy reaches 80% under the mobile setting.
We proposed Once for All (OFA), a new methodology that decouples model training from architecture search for efficient deep learning deployment under a large number of deployment scenarios.
Unlike previous approaches that design and train a neural network for each deployment scenario, we designed a once-for-all network that supports different architectural configurations, including elastic depth, width, kernel size, and resolution.
It greatly reduces the training cost (GPU hours, energy Specialized deployment results on CPU, GPU, mGPU, and FPGA accelerator.
Specialized models by OFA consistently achieve significantly higher ImageNet accuracy with similar latency than non-specialized neural networks.
More remarkably, specializing for a new hardware platform does not add training cost using OFA.
consumption, and CO 2 emission) compared to conventional methods.
To prevent sub-networks of different sizes from interference, we proposed a progressive shrinking algorithm that enables a large number of sub-network to achieve the same level of accuracy compared to training them independently.
Experiments on a diverse range of hardware platforms and efficiency constraints demonstrated the effectiveness of our approach.
We introduce techniques to train a single once-for-all network that fits many hardware platforms.