Google Open Sources TensorFlow Version of MorphNet DL Tool

By John Russell

April 18, 2019

Designing optimum deep neural networks remains a non-trivial exercise. “Given the large search space of possible architectures, designing a network from scratch for your specific application can be prohibitively expensive in terms of computational resources and time,” write Andrew Poon and Dhyanesh Narayanan on Google’s Research blog. “Approaches such as Neural Architecture Search and AdaNet use machine learning to search the design space in order to find improved architectures. An alternative is to take an existing architecture for a similar problem and, in one shot, optimize it for the task at hand.”

In the blog, they announce Google has open sourced a TensorFlow implementation of its MorphNet tool which permits taking an existing DNN developed for one problem and rapidly adopting it for another. “MorphNet takes an existing neural network as input and produces a new neural network that is smaller, faster, and yields better performance tailored to a new problem. We’ve applied the technique to Google-scale problems to design production-serving networks that are both smaller and more accurate, and now we have open sourced the TensorFlow implementation of MorphNet to the community so that you can use it to make your models more efficient,” they write.

For DNN developers and users, the new tool could save time and simplify networks.

“MorphNet optimizes a neural network through a cycle of shrinking and expanding phases,” write Poon and Narayanan. “In the shrinking phase, MorphNet identifies inefficient neurons and prunes them from the network by applying a sparsifying regularizer such that the total loss function of the network includes a cost for each neuron. However, rather than applying a uniform cost per neuron, MorphNet calculates a neuron cost with respect to the targeted resource. As training progresses, the optimizer is aware of the resource cost when calculating gradients, and thus learns which neurons are resource-efficient and which can be removed.”

Poon and Narayanan present several examples and bullet out the following “four key value propositions offered by MorphNet:”

  • Targeted Regularization. The approach that MorphNet takes towards regularization is more intentional than other sparsifying regularizers. In particular, the MorphNet approach to induce better sparsification is targeted at the reduction of a particular resource (such as FLOPs per inference or model size). This enables better control of the network structures induced by MorphNet, which can be markedly different depending on the application domain and associated constraints.For example, the left panel of the figure below presents a baseline network with the commonly used ResNet-101 architecture trained on JFT. The structures generated by MorphNet when targeting FLOPs (center, with 40% fewer FLOPs) or model size (right, with 43% fewer weights) are dramatically different. When optimizing for computation cost, higher-resolution neurons in the lower layers of the network tend to be pruned more than lower-resolution neurons in the upper layers. When targeting smaller model size, the pruning tradeoff is the opposite.
Targeted Regularization by MorphNet. Rectangle width is proportional to the number of channels in the layer. The purple bar at the bottom is the input layer. Left: Baseline network used as input to MorphNet. Center: Output applying FLOP regularizer. Right: Output applying size regularizer.
  • Topology Morphing. As MorphNet learns the number of neurons per layer, the algorithm could encounter a special case of sparsifying all the neurons in a layer. When a layer has 0 neurons, this effectively changes the topology of the network by cutting the affected branch from the network.
  • Scalability. MorphNet learns the new structure in a single training run and is a great approach when your training budget is limited. MorphNet can also be applied directly to expensive networks and datasets.
  • Portability. MorphNet produces networks that are “portable” in the sense that they are intended to be retrained from scratch and the weights are not tied to the architecture learning procedure. You don’t have to worry about copying checkpoints or following special training recipes. Simply train your new network as you normally would.
MorphNet applied to Inception V2 on ImageNet. Applying the flop regularizer alone (blue) improves the performance relative to baseline (red) by 11-15%. A full cycle, including both the regularizer and width multiplier, yields an increase in accuracy for the same cost (“x1”; purple), with continued improvement from a second cycle (“x2”; cyan).

“As a demonstration, we applied MorphNet to Inception V2 trained on ImageNet by targeting FLOPs. The baseline approach is to use a width multiplier to trade off accuracy and FLOPs by uniformly scaling down the number of outputs for each convolution (red). The MorphNet approach targets FLOPs directly and produces a better trade-off curve when shrinking the model (blue). In this case, FLOP cost is reduced 11% to 15% with the same accuracy as compared to the baseline,” write the researchers.

They conclude with, “We’ve applied MorphNet to several production-scale image processing models at Google. Using MorphNet resulted in significant reduction in model-size/FLOPs with little to no loss in quality. We invite you to try MorphNet—the open source TensorFlow implementation can be found here, and you can also read the MorphNet paper for more details.”

Link to Google blog (MorphNet: Towards Faster and Smaller Neural Networks): https://ai.googleblog.com

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

ISC19 Cluster Competition: Application Results, Finally!

July 15, 2019

Our exhaustive coverage of the ISC19 Student Cluster Competition continues as we discuss the application scores below. While the scores were typically high, some of the apps, like SWIFT and OpenFOAM, really pushed the st Read more…

By Dan Olds

Portugal Launches Its First Supercomputer

July 12, 2019

Portugal has officially inaugurated its first-ever supercomputer. The unassumingly named “Bob” supercomputer is housed in the Minho Advanced Computer Center (MACC) at the University of Minho.  Bob was announced i Read more…

By Oliver Peckham

What’s New in HPC Research: Traffic Simulation, Performance Variations, Scheduling & More

July 11, 2019

In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

For decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

How AI Powers Up Data Management and Analytics

Companies are making more decisions based on data. However, the ability to intelligently process the growing volume of data is a bottleneck to extracting actionable insights. Read more…

Nvidia Expands DGX-Ready AI Program to 19 Countries

July 11, 2019

Nvidia’s DGX-Ready Data Center Program, announced in January and designed to provide colo and public cloud-like options to access the company’s GPU-powered servers for AI workloads, has expanded the program beyond th Read more…

By Doug Black

ISC19 Cluster Competition: Application Results, Finally!

July 15, 2019

Our exhaustive coverage of the ISC19 Student Cluster Competition continues as we discuss the application scores below. While the scores were typically high, som Read more…

By Dan Olds

Nvidia Expands DGX-Ready AI Program to 19 Countries

July 11, 2019

Nvidia’s DGX-Ready Data Center Program, announced in January and designed to provide colo and public cloud-like options to access the company’s GPU-powered Read more…

By Doug Black

Argonne Team Makes Record Globus File Transfer

July 10, 2019

A team of scientists at Argonne National Laboratory has broken a data transfer record by moving a staggering 2.9 petabytes of data for a research project.  The data – from three large cosmological simulations – was generated and stored on the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF)... Read more…

By Oliver Peckham

Nvidia, Google Tie in Second MLPerf Training ‘At-Scale’ Round

July 10, 2019

Results for the second round of the AI benchmarking suite known as MLPerf were published today with Google Cloud and Nvidia each picking up three wins in the at Read more…

By Tiffany Trader

Applied Materials Embedding New Memory Technologies in Chips

July 9, 2019

Applied Materials, the $17 billion Santa Clara-based materials engineering company for the semiconductor industry, today announced manufacturing systems enablin Read more…

By Doug Black

ISC19 Cluster Competition: HPCC Deep Dive

July 7, 2019

The biggest benchmark the student warriors tackled during the ISC19 Student Cluster Competition was the colossal HPC Challenge. This is a collection of benchmar Read more…

By Dan Olds

OLCF Bids Farewell to Its Titan Supercomputer

July 4, 2019

After seven years of faithful service, and a long reign as the United States' fastest supercomputer, the Cray XK7-based Titan supercomputer at the Oak Ridge Lea Read more…

By Staff report

Quantum Bits: Neven’s Law (Who Asked for That), D-Wave’s Steady Push, IBM’s Li-O2- Simulation

July 3, 2019

Quantum computing’s (QC) many-faceted R&D train keeps slogging ahead and recently Japan is taking a leading role. Yesterday D-Wave Systems announced it ha Read more…

By John Russell

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
GOOGLE
GOOGLE
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

A Behind-the-Scenes Look at the Hardware That Powered the Black Hole Image

June 24, 2019

Two months ago, the first-ever image of a black hole took the internet by storm. A team of scientists took years to produce and verify the striking image – an Read more…

By Oliver Peckham

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

Chinese Company Sugon Placed on US ‘Entity List’ After Strong Showing at International Supercomputing Conference

June 26, 2019

After more than a decade of advancing its supercomputing prowess, operating the world’s most powerful supercomputer from June 2013 to June 2018, China is keep Read more…

By Tiffany Trader

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This