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EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML
Tuesday, August 6, 2019
Posted by Suyog Gupta, Machine Learning Accelerator Architect and Mingxing Tan, Software Engineer, Google Research
For several decades, computer processors have doubled their performance every couple of years by reducing the size of the transistors inside each chip, as described by
Moore’s Law
. As reducing transistor size becomes more and more difficult, there is a renewed focus in the industry on developing domain-specific architectures — such as
hardware accelerators
— to continue advancing computational power. This is especially true for machine learning, where efforts are aimed at building specialized architectures for neural network (NN) acceleration. Ironically, while there has been a steady proliferation of these architectures in data centers and on
edge computing
platforms, the NNs that run on them are rarely customized to take advantage of the underlying hardware.
Today, we are happy to announce the release of
EfficientNet-EdgeTPU
, a family of image classification models derived from
EfficientNets
, but customized to run optimally on Google’s
Edge TPU
, a power-efficient
hardware accelerator available to developers through the
Coral Dev Board
and a
USB Accelerator
. Through such model customizations, the Edge TPU is able to provide real-time image classification performance while simultaneously achieving accuracies typically seen only when running much larger, compute-heavy models in data centers.
Using AutoML to customize EfficientNets for Edge TPU
EfficientNets
have been shown to achieve state-of-the-art accuracy in image classification tasks while significantly reducing the model size and computational complexity. To build EfficientNets designed to leverage the Edge TPU’s accelerator architecture, we invoked the
AutoML MNAS framework
and augmented the original EfficientNet’s neural network architecture search space with building blocks that execute efficiently on the Edge TPU (discussed below). We also built and integrated a “latency predictor” module that provides an estimate of the model latency when executing on the Edge TPU, by running the models on a cycle-accurate architectural simulator. The AutoML MNAS controller implements a
reinforcement learning
algorithm to search this space while attempting to maximize the reward, which is a joint function of the predicted latency and model accuracy. From past experience, we know that Edge TPU’s power efficiency and performance tend to be maximized when the model fits within its on-chip memory. Hence we also modified the reward function to generate a higher reward for models that satisfy this constraint.
Overall AutoML flow for designing customized EfficientNet-EdgeTPU models.
Search Space Design
When performing the architecture search described above, one must consider that EfficientNets rely primarily on
depthwise-separable convolutions
, a type of neural network block that factorizes a regular convolution to reduce the number of parameters as well as the amount of computations. However, for certain configurations, a regular convolution utilizes the Edge TPU architecture more efficiently and executes faster, despite the much larger amount of compute. While it is possible, albeit tedious, to manually craft a network that uses an optimal combination of the different building blocks, augmenting the AutoML search space with these accelerator-optimal blocks is a more scalable approach.
A regular 3x3 convolution (right) has more compute (multiply-and-accumulate (mac) operations) than an depthwise-separable convolution (left), but for certain input/output shapes, executes faster on Edge TPU due to ~3x more effective hardware utilization.
In addition, removing certain operations from the search space that require modifications to the Edge TPU compiler to fully support, such
swish non-linearity
and
squeeze-and-excitation block
, naturally leads to models that are readily ported to the Edge TPU hardware. These operations tend to improve model quality slightly, so by eliminating them from the search space, we have effectively instructed AutoML to discover alternate network architectures that may compensate for any potential loss in quality.
Model Performance
The neural architecture search (NAS) described above produced a baseline model,
EfficientNet-EdgeTPU-S
, which is subsequently scaled up using EfficientNet's
compound scaling
method to produce the
-M
and
-L
models. The compound scaling approach selects an optimal combination of input image resolution scaling, network width, and depth scaling to construct larger, more accurate models. The
-M
, and
-L
models achieve higher accuracy at the cost of increased latency as shown in the figure below.
EfficientNet-EdgeTPU-S/M/L models achieve better latency and accuracy than existing EfficientNets (B1),
ResNet
, and
Inception
by specializing the network architecture for Edge TPU hardware. In particular, our EfficientNet-EdgeTPU-S achieves higher accuracy, yet runs 10x faster than ResNet-50.
Interestingly, the NAS-generated model employs the regular convolution quite extensively in the initial part of the network where the depthwise-separable convolution tends to be less effective than the regular convolution when executed on the accelerator. This clearly highlights the fact that trade-offs usually made while optimizing models for general purpose CPUs (reducing the total number of operations, for example) are not necessarily optimal for hardware accelerators. Also, these models achieve high accuracy even without the use of esoteric operations. Comparing with the other image classification models such as Inception-resnet-v2 and Resnet50, EfficientNet-EdgeTPU models are not only more accurate, but also run faster on Edge TPUs.
This work represents a first experiment in building accelerator-optimized models using AutoML. The AutoML-based model customization can be extended to not only a wide range of hardware accelerators, but also to several different applications that rely on neural networks.
From Cloud TPU training to Edge TPU deployment
We have released the training code and pretrained models for EfficientNet-EdgeTPU on our
github repository
. We employ tensorflow’s
post-training quantization tool
to convert a floating-point trained model to an Edge TPU-compatible integer-quantized model. For these models, the post-training quantization works remarkably well and produces only a very slight loss in accuracy (~0.5%). The script for exporting the quantized model from a training checkpoint can be found
here
. For an update on the Coral platform, see
this post on the Google Developer’s Blog
, and for full reference materials and detailed instructions, please refer to the
Coral website
.
Acknowledgements
Special thanks to Quoc Le, Hongkun Yu, Yunlu Li, Ruoming Pang, and Vijay Vasudevan from the Google Brain team; Bo Wu, Vikram Tank, and Ajay Nair from the Google Coral team; Han Vanholder, Ravi Narayanaswami, John Joseph, Dong Hyuk Woo, Raksit Ashok, Jason Jong Kyu Park, Jack Liu, Mohammadali Ghodrat, Cao Gao, Berkin Akin, Liang-Yun Wang, Chirag Gandhi, and Dongdong Li from the Google Edge TPU team.
Applying AutoML to Transformer Architectures
Friday, June 14, 2019
Posted by David So, Software Engineer, Google AI
Since it was introduced a few years ago, Google’s
Transformer architecture
has been applied to challenges ranging from
generating fantasy fiction
to
writing musical harmonies
. Importantly, the Transformer’s high performance has demonstrated that
feed forward neural networks
can be as effective as
recurrent neural networks
when applied to sequence tasks, such as language modeling and translation. While the Transformer and
other feed forward models
used for sequence problems are rising in popularity, their architectures are almost exclusively manually designed, in contrast to the computer vision domain where
AutoML
approaches have found
state-of-the-art models
that outperform those that are designed by hand. Naturally, we wondered if the application of AutoML in the sequence domain could be equally successful.
After conducting an
evolution-based
neural architecture search (NAS), using translation as a proxy for sequence tasks in general, we found the
Evolved Transformer
, a new Transformer architecture that demonstrates promising improvements on a variety of
natural language processing
(NLP) tasks. Not only does the Evolved Transformer achieve state-of-the-art translation results, but it also demonstrates improved performance on language modeling when compared to the original Transformer. We are
releasing this new model
as part of
Tensor2Tensor
, where it can be used for any sequence problem.
Developing the Techniques
To begin the evolutionary NAS, it was necessary for us to develop new techniques, due to the fact that the task used to evaluate the “fitness” of each architecture,
WMT’14 English-German
translation, is computationally expensive. This makes the searches more expensive than similar searches executed in the vision domain, which can leverage smaller datasets, like
CIFAR-10
. The first of these techniques is warm starting—seeding the initial evolution population with the Transformer architecture instead of random models. This helps ground the search in an area of the search space we know is strong, thereby allowing it to find better models faster.
The second technique is a new method we developed called Progressive Dynamic Hurdles (PDH), an algorithm that augments the evolutionary search to allocate more resources to the strongest candidates, in contrast to previous works, where each candidate model of the NAS is allocated the same amount of resources when it is being evaluated. PDH allows us to terminate the evaluation of a model early if it is flagrantly bad, allowing promising architectures to be awarded more resources.
The Evolved Transformer
Using these methods, we conducted a large-scale NAS on our translation task and discovered the Evolved Transformer (ET). Like most sequence to sequence (seq2seq) neural network architectures, it has an encoder that encodes the input sequence into embeddings and a decoder that uses those embeddings to construct an output sequence; in the case of translation, the input sequence is the sentence to be translated and the output sequence is the translation.
The most interesting feature of the Evolved Transformer is the
convolutional layers
at the bottom of both its encoder and decoder modules that were added in a similar branching pattern in both places (i.e. the inputs run through two separate convolutional layers before being added together).
A comparison between the Evolved Transformer and the original Transformer encoder architectures. Notice the branched convolution structure at the bottom of the module, which formed in both the encoder and decoder independently. See
our paper
for a description of the decoder.
This is particularly interesting because the encoder and decoder architectures are not shared during the NAS, so this architecture was independently discovered as being useful in both the encoder and decoder, speaking to the strength of this design. Whereas the original Transformer relied solely on self-attention, the Evolved Transformer is a hybrid, leveraging the strengths of both self-attention and wide convolution.
Evaluation of the Evolved Transformer
To test the effectiveness of this new architecture, we first compared it to the original Transformer on the English-German translation task we used during the search. We found that the Evolved Transformer had better
BLEU
and
perplexity performance
at all parameter sizes, with the biggest gain at the size compatible with mobile devices (~7 million parameters), demonstrating an efficient use of parameters. At a larger size, the Evolved Transformer reaches state-of-the-art performance on WMT’ 14 En-De with a BLEU score of 29.8 and a SacreBLEU score of 29.2.
Comparison between the Evolved Transformer and the original Transformer on WMT’14 En-De at varying sizes. The biggest gains in performance occur at smaller sizes, while ET also shows strength at larger sizes, outperforming the largest Transformer with 37.6% less parameters (models to compare are circled in green). See Table 3 in
our paper
for the exact numbers.
To test generalizability, we also compared ET to the Transformer on additional NLP tasks. First, we looked at translation using different language pairs, and found ET demonstrated improved performance, with margins similar to those seen on English-German; again, due to its efficient use of parameters, the biggest improvements were observed for medium sized models. We also compared the decoders of both models on language modeling using
LM1B
, and saw a performance improvement of nearly 2 perplexity.
Future Work
These results are the first step in exploring the application of architecture search to feed forward sequence models. The Evolved Transformer is being
open sourced
as part of
Tensor2Tensor
, where it can be used for any sequence problem. To promote reproducibility, we are also open sourcing the
search space we used for our search
and a
Colab
with an implementation of Progressive Dynamic Hurdles. We look forward to seeing what the research community does with the new model and hope that others are able to build off of these new search techniques!
EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
Wednesday, May 29, 2019
Posted by Mingxing Tan, Staff Software Engineer and Quoc V. Le, Principal Scientist, Google AI
Convolutional neural networks
(CNNs) are commonly developed at a fixed resource cost, and then scaled up in order to achieve better accuracy when more resources are made available. For example,
ResNet
can be scaled up from ResNet-18 to ResNet-200 by increasing the number of layers, and recently,
GPipe
achieved 84.3%
ImageNet
top-1 accuracy by scaling up a baseline CNN by a factor of four. The conventional practice for model scaling is to arbitrarily increase the CNN depth or width, or to use larger input image resolution for training and evaluation. While these methods do improve accuracy, they usually require tedious manual tuning, and still often yield suboptimal performance. What if, instead, we could find a more principled method to scale up a CNN to obtain better accuracy and efficiency?
In our
ICML 2019
paper, “
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
”, we propose a novel model scaling method that uses a simple yet highly effective
compound coefficient
to scale up CNNs in a more structured manner. Unlike conventional approaches that arbitrarily scale network dimensions, such as width, depth and resolution, our method uniformly scales each dimension with a fixed set of scaling coefficients. Powered by this novel scaling method and recent progress on
AutoML
, we have developed a family of models, called EfficientNets, which superpass state-of-the-art accuracy with up to 10x better efficiency (smaller and faster).
Compound Model Scaling: A Better Way to Scale Up CNNs
In order to understand the effect of scaling the network, we systematically studied the impact of scaling different dimensions of the model. While scaling individual dimensions improves model performance, we observed that balancing all dimensions of the network—width, depth, and image resolution—against the available resources would best improve overall performance.
The first step in the compound scaling method is to perform a
grid search
to find the relationship between different scaling dimensions of the baseline network under a fixed resource constraint (e.g., 2x more
FLOPS
).This determines the appropriate scaling coefficient for each of the dimensions mentioned above. We then apply those coefficients to scale up the baseline network to the desired target model size or computational budget.
Comparison of different scaling methods. Unlike conventional scaling methods (b)-(d) that arbitrary scale a single dimension of the network, our compound scaling method uniformly scales up all dimensions in a principled way.
This compound scaling method consistently improves model accuracy and efficiency for scaling up existing models such as
MobileNet
(+1.4% imagenet accuracy), and
ResNet
(+0.7%), compared to conventional scaling methods.
EfficientNet Architecture
The effectiveness of model scaling also relies heavily on the baseline network. So, to further improve performance, we have also developed a new baseline network by performing a
neural architecture search
using the
AutoML MNAS framework
, which optimizes both accuracy and efficiency (FLOPS). The resulting architecture uses mobile inverted bottleneck convolution (MBConv), similar to
MobileNetV2
and
MnasNet
, but is slightly larger due to an increased FLOP budget. We then scale up the baseline network to obtain a family of models, called
EfficientNets
.
The architecture for our baseline network EfficientNet-B0 is simple and clean, making it easier to scale and generalize.
EfficientNet Performance
We have compared our EfficientNets with other existing CNNs on
ImageNet
. In general, the EfficientNet models achieve both higher accuracy and better efficiency over existing CNNs, reducing parameter size and FLOPS by an order of magnitude. For example, in the high-accuracy regime, our EfficientNet-B7 reaches state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on CPU inference than the previous
Gpipe
. Compared with the widely used
ResNet-50
, our EfficientNet-B4 uses similar FLOPS, while improving the top-1 accuracy from 76.3% of ResNet-50 to 82.6% (+6.3%).
Model Size vs. Accuracy Comparison. EfficientNet-B0 is the baseline network developed by
AutoML MNAS
, while Efficient-B1 to B7 are obtained by scaling up the baseline network. In particular, our EfficientNet-B7 achieves new state-of-the-art 84.4% top-1 / 97.1% top-5 accuracy, while being 8.4x smaller than the best existing CNN.
Though EfficientNets perform well on ImageNet, to be most useful, they should also transfer to other datasets. To evaluate this, we tested EfficientNets on eight widely used transfer learning datasets. EfficientNets achieved state-of-the-art accuracy in 5 out of the 8 datasets, such as
CIFAR-100
(91.7%) and
Flowers
(98.8%), with an order of magnitude fewer parameters (up to 21x parameter reduction), suggesting that our EfficientNets also transfer well.
By providing significant improvements to model efficiency, we expect EfficientNets could potentially serve as a new foundation for future computer vision tasks. Therefore, we have open-sourced all EfficientNet models, which we hope can benefit the larger machine learning community. You can find the EfficientNet source code and
TPU
training scripts
here
.
Acknowledgements:
Special thanks to Hongkun Yu, Ruoming Pang, Vijay Vasudevan, Alok Aggarwal, Barret Zoph, Xianzhi Du, Xiaodan Song, Samy Bengio, Jeff Dean, and the Google Brain team.
An End-to-End AutoML Solution for Tabular Data at KaggleDays
Thursday, May 9, 2019
Posted by Yifeng Lu, Software Engineer, Google AI
Machine learning (ML) for tabular data (e.g. spreadsheet data) is one of the most active research areas in both ML research and business applications. Solutions to tabular data problems, such as fraud detection and inventory prediction, are critical for many business sectors, including retail, supply chain, finance, manufacturing, marketing and others. Current ML-based solutions to these problems can be achieved by those with significant ML expertise, including manual
feature engineering
and
hyper-parameter tuning
, to create a good model. However, the lack of broad availability of these skills limits the efficiency of business improvements through ML.
Google’s AutoML efforts aim to make ML more scalable and accelerate both research and industry applications. Our initial efforts of
neural architecture search
have enabled breakthroughs in computer vision with
NasNet
, and evolutionary methods such as
AmoebaNet
and hardware-aware mobile vision architecture
MNasNet
further show the benefit of these learning-to-learn methods. Recently, we applied a learning-based approach to tabular data, creating a scalable end-to-end AutoML solution that meets three key criteria:
Full automation
: Data and computation resources are the only inputs, while a servable TensorFlow model is the output. The whole process requires no human intervention.
Extensive coverage
: The solution is applicable to the majority of arbitrary tasks in the tabular data domain.
High quality:
Models generated by AutoML has comparable quality to models manually crafted by top ML experts.
To benchmark our solution, we entered our algorithm in the
KaggleDays SF Hackathon
, an 8.5 hour competition of 74 teams with up to 3 members per team, as part of the
KaggleDays event
. The first time that
AutoML has competed against Kaggle participants, the competition involved predicting manufacturing defects given information about the material properties and testing results for batches of automotive parts. Despite competing against participants thats were at the
Kaggle progression system
Master level, including many who were at the GrandMaster level, our team (“Google AutoML”) led for most of the day and ended up
finishing second place by a narrow margin
, as seen in the
final leaderboard
.
Our team’s AutoML solution was a multistage TensorFlow pipeline. The first stage is responsible for automatic feature engineering,
architecture search
, and hyperparameter tuning through search. The promising models from the first stage are fed into the second stage, where cross validation and
bootstrap aggregating
are applied for better model selection. The best models from the second stage are then combined in the final model.
The workflow for the “Google AutoML” team was quite different from that of other Kaggle competitors. While they were busy with analyzing data and experimenting with various feature engineering ideas, our team spent most of time monitoring jobs and and waiting for them to finish. Our solution for second place on the final leaderboard required 1 hour on 2500 CPUs to finish end-to-end.
After the competition, Kaggle published a
public kernel
to investigate winning solutions and found that augmenting the top hand-designed models with AutoML models, such as ours, could be a useful way for ML experts to create even better performing systems. As can be seen in the plot below, AutoML has the potential to enhance the efforts of human developers and address a broad range of ML problems.
Potential model quality improvement on final leaderboard if AutoML models were merged with other Kagglers’ models. “Erkut & Mark, Google AutoML”, includes the top winner “Erkut & Mark” and the second place “Google AutoML” models. Erkut Aykutlug and Mark Peng used
XGBoost
with creative
feature engineering
whereas AutoML uses both neural network and gradient boosting tree (
TFBT
) with automatic feature engineering and hyperparameter tuning.
Google Cloud AutoML Tables
The solution we presented at the competitions is the main algorithm in
Google Cloud AutoML Tables
, which was recently launched (beta) at
Google Cloud Next ‘19
.
The AutoML Tables implementation regularly performs well in benchmark tests against Kaggle competitions as shown in the plot below, demonstrating state-of-the-art performance across the industry.
Third party benchmark of AutoML Tables on multiple Kaggle competitions
We are excited about the potential application of AutoML methods across a wide range of real business problems. Customers have already been leveraging their tabular enterprise data to tackle mission-critical tasks like supply chain management and lead conversion optimization using AutoML Tables, and we are excited to be providing our state-of-the-art models to solve tabular data problems.
Acknowledgements
This project was only possible thanks to Google Brain team members Ming Chen, Da Huang, Yifeng Lu, Quoc V. Le and Vishy Tirumalashetty. We also thank Dawei Jia, Chenyu Zhao and Tin-yun Ho from the Cloud AutoML Tables team for great infrastructure and product landing collaboration. Thanks to Walter Reade, Julia Elliott and Kaggle for organizing such an engaging competition.
Using Machine Learning to Explore Neural Network Architecture
Wednesday, May 17, 2017
Posted by Quoc Le & Barret Zoph, Research Scientists, Google Brain team
At Google, we have successfully applied deep learning models to many applications, from
image recognition
to
speech recognition
to
machine translation
. Typically, our machine learning models are painstakingly designed by a team of engineers and scientists. This process of manually designing machine learning models is difficult because the search space of all possible models can be combinatorially large — a typical 10-layer network can have ~10
10
candidate networks! For this reason, the process of designing networks often takes a significant amount of time and experimentation by those with significant machine learning expertise.
Our
GoogleNet
architecture. Design of this network required many years of careful experimentation and refinement from initial versions of convolutional architectures.
To make this process of designing machine learning models much more accessible, we’ve been exploring ways to automate the design of machine learning models. Among many algorithms we’ve studied,
evolutionary algorithms
[1] and
reinforcement learning algorithms
[2] have shown great promise. But in this blog post, we’ll focus on our reinforcement learning approach and the early results we’ve gotten so far.
In our approach (which we call "AutoML"), a controller neural net can propose a “child” model architecture, which can then be trained and evaluated for quality on a particular task. That feedback is then used to inform the controller how to improve its proposals for the next round. We repeat this process thousands of times — generating new architectures, testing them, and giving that feedback to the controller to learn from. Eventually the controller learns to assign high probability to areas of architecture space that achieve better accuracy on a held-out validation dataset, and low probability to areas of architecture space that score poorly. Here’s what the process looks like:
We’ve applied this approach to two heavily benchmarked datasets in deep learning: image recognition with
CIFAR-10
and language modeling with
Penn Treebank
. On both datasets, our approach can design models that achieve accuracies on par with state-of-art models designed by machine learning experts (including some on our own team!).
So, what kind of neural nets does it produce? Let’s take one example: a recurrent architecture that’s trained to predict the next word on the Penn Treebank dataset. On the left here is a neural net designed by human experts. On the right is a recurrent architecture created by our method:
The machine-chosen architecture does share some common features with the human design, such as using addition to combine input and previous hidden states. However, there are some notable new elements — for example, the machine-chosen architecture incorporates a multiplicative combination (the left-most blue node on the right diagram labeled “
elem_mult
”). This type of combination is not common for recurrent networks, perhaps because researchers see no obvious benefit for having it. Interestingly, a simpler form of this approach was
recently suggested
by human designers, who also argued that this multiplicative combination can actually alleviate gradient vanishing/exploding issues, suggesting that the machine-chosen architecture was able to discover a useful new neural net architecture.
This approach may also teach us something about why certain types of neural nets work so well. The architecture on the right here has many channels so that the gradient can flow backwards, which may help explain why
LSTM RNNs
work better than standard
RNNs
.
Going forward, we’ll work on careful analysis and testing of these machine-generated architectures to help refine our understanding of them. If we succeed, we think this can inspire new types of neural nets and make it possible for non-experts to create neural nets tailored to their particular needs, allowing machine learning to have a greater impact to everyone.
References
[1]
Large-Scale Evolution of Image Classifiers
,
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Quoc Le, Alex Kurakin. International Conference on Machine Learning, 2017.
[2]
Neural Architecture Search with Reinforcement Learning
,
Barret Zoph, Quoc V. Le. International Conference on Learning Representations, 2017.
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