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!

Army Seeks AI Ground Truth

April 3, 2020

Deep neural networks are being mustered by U.S. military researchers to marshal new technology forces on the Internet of Battlefield Things. U.S. Army and industry researchers said this week they have developed a “c Read more…

By George Leopold

Piz Daint Tackles Marsquakes

April 3, 2020

Even as researchers use supercomputers to probe the mysteries of earthquakes here on Earth, others are setting their sights on quakes just a little farther away. Researchers at ETH Zürich in Switzerland have applied sup Read more…

By Oliver Peckham

HPC Career Notes: April 2020 Edition

April 2, 2020

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high-performance computing community. Whether it’s a promotion, new company hire, or even an accolade, we’ Read more…

By Mariana Iriarte

AMD Epyc CPUs Now on Bare Metal IBM Cloud Servers

April 1, 2020

AMD’s expanding presence in the datacenter and cloud computing markets took a step forward with today’s announcement that its 7nm 2nd Gen Epyc 7642 CPUs are now available on IBM Cloud bare metal servers. AMD, whose Read more…

By Doug Black

Supercomputer Testing Probes Viral Transmission in Airplanes

April 1, 2020

It might be a long time before the general public is flying again, but the question remains: how high-risk is air travel in terms of viral infection? In an article for the Texas Advanced Computing Center (TACC), Faith Si Read more…

By Staff report

AWS Solution Channel

Amazon FSx for Lustre Update: Persistent Storage for Long-Term, High-Performance Workloads

Last year I wrote about Amazon FSx for Lustre and told you how our customers can use it to create pebibyte-scale, highly parallel POSIX-compliant file systems that serve thousands of simultaneous clients driving millions of IOPS (Input/Output Operations per Second) with sub-millisecond latency. Read more…

ECP Milestone Report Details Progress and Directions

April 1, 2020

The Exascale Computing Project (ECP) milestone report issued last week presents a good snapshot of progress in preparing applications for exascale computing. There are roughly 30 ECP application development (AD) subproj Read more…

By John Russell

ECP Milestone Report Details Progress and Directions

April 1, 2020

The Exascale Computing Project (ECP) milestone report issued last week presents a good snapshot of progress in preparing applications for exascale computing. Th Read more…

By John Russell

Pandemic ‘Wipes Out’ 2020 HPC Market Growth, Flat to 12% Drop Expected

March 31, 2020

As the world battles the still accelerating novel coronavirus, the HPC community has mounted a forceful response to the pandemic on many fronts. But these efforts won't inoculate the HPC industry from the economic effects of COVID-19. Market watcher Intersect360 Research has revised its 2020 forecast for HPC products and services, projecting... Read more…

By Tiffany Trader

LLNL Leverages Supercomputing to Identify COVID-19 Antibody Candidates

March 30, 2020

As COVID-19 sweeps the globe to devastating effect, supercomputers around the world are spinning up to fight back by working on diagnosis, epidemiology, treatme Read more…

By Staff report

Weather at Exascale: Load Balancing for Heterogeneous Systems

March 30, 2020

The first months of 2020 were dominated by weather and climate supercomputing news, with major announcements coming from the UK, the European Centre for Medium- Read more…

By Oliver Peckham

Q&A Part Two: ORNL’s Pooser on Progress in Quantum Communication

March 30, 2020

Quantum computing seems to get more than its fair share of attention compared to quantum communication. That’s despite the fact that quantum networking may be Read more…

By John Russell

DoE Expands on Role of COVID-19 Supercomputing Consortium

March 25, 2020

After announcing the launch of the COVID-19 High Performance Computing Consortium on Sunday, the Department of Energy yesterday provided more details on its sco Read more…

By John Russell

[email protected] Rallies a Legion of Computers Against the Coronavirus

March 24, 2020

Last week, we highlighted [email protected], a massive, crowdsourced computer network that has turned its resources against the coronavirus pandemic sweeping the globe – but [email protected] isn’t the only game in town. The internet is buzzing with crowdsourced computing... Read more…

By Oliver Peckham

Conversation: ANL’s Rick Stevens on DoE’s AI for Science Project

March 23, 2020

With release of the Department of Energy’s AI for Science report in late February, the effort to build a national AI program, modeled loosely on the U.S. Exascale Initiative, enters a new phase. Project leaders have already had early discussions with Congress... Read more…

By John Russell

[email protected] Turns Its Massive Crowdsourced Computer Network Against COVID-19

March 16, 2020

For gamers, fighting against a global crisis is usually pure fantasy – but now, it’s looking more like a reality. As supercomputers around the world spin up Read more…

By Oliver Peckham

Julia Programming’s Dramatic Rise in HPC and Elsewhere

January 14, 2020

Back in 2012 a paper by four computer scientists including Alan Edelman of MIT introduced Julia, A Fast Dynamic Language for Technical Computing. At the time, t Read more…

By John Russell

Global Supercomputing Is Mobilizing Against COVID-19

March 12, 2020

Tech has been taking some heavy losses from the coronavirus pandemic. Global supply chains have been disrupted, virtually every major tech conference taking place over the next few months has been canceled... Read more…

By Oliver Peckham

[email protected] Rallies a Legion of Computers Against the Coronavirus

March 24, 2020

Last week, we highlighted [email protected], a massive, crowdsourced computer network that has turned its resources against the coronavirus pandemic sweeping the globe – but [email protected] isn’t the only game in town. The internet is buzzing with crowdsourced computing... Read more…

By Oliver Peckham

DoE Expands on Role of COVID-19 Supercomputing Consortium

March 25, 2020

After announcing the launch of the COVID-19 High Performance Computing Consortium on Sunday, the Department of Energy yesterday provided more details on its sco Read more…

By John Russell

Steve Scott Lays Out HPE-Cray Blended Product Roadmap

March 11, 2020

Last week, the day before the El Capitan processor disclosures were made at HPE's new headquarters in San Jose, Steve Scott (CTO for HPC & AI at HPE, and former Cray CTO) was on-hand at the Rice Oil & Gas HPC conference in Houston. He was there to discuss the HPE-Cray transition and blended roadmap, as well as his favorite topic, Cray's eighth-gen networking technology, Slingshot. Read more…

By Tiffany Trader

Fujitsu A64FX Supercomputer to Be Deployed at Nagoya University This Summer

February 3, 2020

Japanese tech giant Fujitsu announced today that it will supply Nagoya University Information Technology Center with the first commercial supercomputer powered Read more…

By Tiffany Trader

Tech Conferences Are Being Canceled Due to Coronavirus

March 3, 2020

Several conferences scheduled to take place in the coming weeks, including Nvidia’s GPU Technology Conference (GTC) and the Strata Data + AI conference, have Read more…

By Alex Woodie

Leading Solution Providers

SC 2019 Virtual Booth Video Tour

AMD
AMD
ASROCK RACK
ASROCK RACK
AWS
AWS
CEJN
CJEN
CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
IBM
IBM
MELLANOX
MELLANOX
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
SIX NINES IT
SIX NINES IT
VERNE GLOBAL
VERNE GLOBAL
WEKAIO
WEKAIO

Cray to Provide NOAA with Two AMD-Powered Supercomputers

February 24, 2020

The United States’ National Oceanic and Atmospheric Administration (NOAA) last week announced plans for a major refresh of its operational weather forecasting supercomputers, part of a 10-year, $505.2 million program, which will secure two HPE-Cray systems for NOAA’s National Weather Service to be fielded later this year and put into production in early 2022. Read more…

By Tiffany Trader

Exascale Watch: El Capitan Will Use AMD CPUs & GPUs to Reach 2 Exaflops

March 4, 2020

HPE and its collaborators reported today that El Capitan, the forthcoming exascale supercomputer to be sited at Lawrence Livermore National Laboratory and serve Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

IBM Unveils Latest Achievements in AI Hardware

December 13, 2019

“The increased capabilities of contemporary AI models provide unprecedented recognition accuracy, but often at the expense of larger computational and energet Read more…

By Oliver Peckham

TACC Supercomputers Run Simulations Illuminating COVID-19, DNA Replication

March 19, 2020

As supercomputers around the world spin up to combat the coronavirus, the Texas Advanced Computing Center (TACC) is announcing results that may help to illumina Read more…

By Staff report

IBM Debuts IC922 Power Server for AI Inferencing and Data Management

January 28, 2020

IBM today launched a Power9-based inference server – the IC922 – that features up to six Nvidia T4 GPUs, PCIe Gen 4 and OpenCAPI connectivity, and can accom Read more…

By John Russell

Summit Joins the Fight Against the Coronavirus

March 6, 2020

With the coronavirus sweeping the globe, tech conferences and supply chains are being hit hard – but now, tech is hitting back. Oak Ridge National Laboratory Read more…

By Staff report

University of Stuttgart Inaugurates ‘Hawk’ Supercomputer

February 20, 2020

This week, the new “Hawk” supercomputer was inaugurated in a ceremony at the High-Performance Computing Center of the University of Stuttgart (HLRS). Offici Read more…

By Staff report

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