Interdisciplinary Team at UT Austin Envisions New Neuromorphic Computing Architecture

November 26, 2019

Nov. 26, 2019 — If you wanted to deliver a package across the street, you could program a powerful computer to do it, equipped with sensors and hardware capable of running multiple differential equations to track the movement and speed of each car. But a young child would be capable of doing the same task with little effort, says Alex Demkov, professor of Physics at The University of Texas at Austin.

Piezoelectric modulation of nonlinear optical response in BaTiO3 thin film. [Credit: Alexander A. Demkov et al, Appl. Phys. Lett. 113, 132902 (2018); doi: 10.1063/1.5045460]
Image courtesy of TACC.
“I guarantee you the brain is completely unaware that differential equations exist. And yet they solve the same problem of how to avoid collisions with fast approaching movement,” Demkov said. “A child trains from a young age, tossing rocks to learn spatial dimensions, and can do things we can scarcely train powerful computers to do.”

Not only that, the brain uses only a fraction of the energy computer chips require to accomplish the task, and can operate at room temperature.

Why is a physicist, with a background in developing new materials for advanced technologies, wondering about a child’s brain? Because his research has recently led him into the world of neuromorphic computing, an emerging field that emulates the workings of the brain to perform the tasks we now use silicon-based digital processing to accomplish.

Researchers in Demkov’s group study new nanomaterials in the UT Material Physics Lab. Image courtesy of TACC.

In the last century, computer processing power has grown rapidly, roughly doubling every two years (according to Moore’s Law), and in the process has transformed our world. But in recent years, progress has slowed as chips reach the physical limits of miniaturization and require unsustainable amounts of electricity to operate.

Alternative paradigms — some new, some old — are getting a fresh look from researchers. Neuromorphic computing is one such paradigm, as is optical computing where light takes the place of electrons as the transmitter of signals. Demkov happens to be an expert in the latter, having worked for two decades developing novel electronic materials for technologies with support from the Air Force, Navy, and National Science Foundation, often in collaboration with IBM and other industry partners.

In recent years, Demkov has been developing hybrid silicon-photonic technologies based on novel nano-scale materials like silicon-integrated barium titanate, a ceramic material that exhibits exotic and useful properties for information processing.

The challenge with optical computing in the past has been the difficulty in making devices that are both controllable and small enough to use in devices. However, barium titanate, Demkov has found, can transmit light and be switched on and off, using a variety of clever mechanisms, using very little power and on a very small scale. Moreover, it can be fused to silicon to integrate into chips that can, for instance, provide the computing and control mechanisms for aircraft at a fraction of the weight of today’s technologies.

Working with IBM and researchers at ETH-Zurich, Demkov recently demonstrated a system that is much more efficient than the current state of the art. The results were published in Nature Materials in 2018.

“Using new materials and old methods, we might be able to create new neuromorphic computers that are better than silicon-based computers at doing certain kinds of transformations,” Demkov said.

Reconsidering the Neuromorphic Canon

The material and optical characteristics of processors are just a few aspects of the problem that need to be solved to develop a rival for silicon semiconductors, which has a century-long head start in research.

To go from a promising new material to a device that can rival and surpass silicon, quantum computers, and a host of other contenders, Demkov has pulled together a team of researchers from across UT Austin and beyond with expertise in neuroscience, algorithmic development, circuit design, parallel computing, and device architectures to create something that exceeds what leading industry groups like Google, Intel, and Hewlett Packard Enterprise are imagining.

“This is our competitive advantage,” he said. “We have 30 brilliant, diverse researchers working on this problem. We have a good idea. And we are not beholden to existing technologies.”

If the use of silicon digital processors is a problem at the technological end of things, troubles at a conceptual level start with the use of a mathematical formulation of thought first enunciated in 1954.

In recent years, Demkov has been developing hybrid silicon-photonic technologies based on novel nano-scale materials. To transmit signals in this optical computing, electrons are replaced with light, which uses less power and space than conventional chips. Image courtesy of TACC.
In recent years, Demkov has been developing hybrid silicon-photonic technologies based on novel nano-scale materials. To transmit signals in this optical computing, electrons are replaced with light, which uses less power and space than conventional chips.

Traditional neuromorphic computing is rooted in the idea that neurons spike, or react, in order to communicate. This spiking takes the place of the on/off, 0/1 of digital gates – the root of computing.

However, neuroscientists have learned a great deal about the reality of neurons in the past six decades, which are much more complicated and interconnected than the old textbook description would lead one to believe.

“Hodgkin Huxley wrote this set of four equations based on the understanding of cell membrane and transfer circa 1954 and that’s what brain science has been about ever since,” Demkov said. “Now we have functional MRI, we have advanced microscopy, so we surely can develop better models.”

Work by Kristen Harris, a leading neuroscientist at UT Austin who uses neuroimaging and computing to create detailed 3D models of the brain, led Demkov to the realization that the mathematical models of neuro-processing are due for a rewrite.

“Brains are not one bit machines,” explained Harris. “Just based on the structure of synapses in the brain in different brain regions and under different activity levels, we’ve seen 26 different distinct synaptic types. It’s not an on off machine. It has graded levels of capability. And is not digital; it’s analog.”

“The simple explanation needs to be rethought and rearticulated as an algorithm,” Demkov concurred.

Harris was drawn to the project because of the diverse perspectives of the researchers involved. “They’re all thinking outside the box. But they didn’t realize how complicated the box was,” she said. “We may not even speak the same language when we start out, but the goal eventually will be to at least understand each other’s language and then begin to build bridges.”

Separate from the behavior and logic of neurons themselves, a key part of the project is the development of improved neural networks — a method of training computers to “learn” how to do human-like tasks, from identifying images to discovering new scientific theories.

Neural networks, when combined with large amounts of data, have shown themselves to be incredibly effective at solving problems that traditional simulation and modeling are incapable of handing. However, today’s state-of-the-art methods are still slow, require massive amounts of computer power, and have been limited in their applications and robustness.

Demkov and the UT team are eyeing new formulations of neural networks that may be able to work faster using non-linear, random connectivity.

“This particular kind of neuromorphic architecture is called the reservoir computer or an echo state machine,” he explained. “It turns out that there’s a way to realize this type of system in optics which is very, very neat. With neuromorphic computing, you didn’t compute anything. You just train the neural network to use spiking to say yes or no.”

Leading the device design effort on the project is Ray Chen, a chair in the Electrical and Computer Engineering department and director of the Nanophotonics and Optical Interconnects Research Lab at the Microelectronics Research Center.

As part of an Air Force-funded research project, Chen has been experimenting with optical neural networks that could use orders-of-magnitude lower power consumption compared to current CPUs and GPUs. At the Asia and South Pacific Design Automation Conference 2019, Chen presented a software-hardware co-designed slim optical neural networks which demonstrates 15 to 38 percent less phase shift variation than state-of-the-art systems with no accuracy loss and better noise robustness. Recently he proposed an optical neural network architecture to perform Fast Fourier transforms — a type of computations frequently used in engineering and science — that could potentially be three times smaller than previous designs with negligible accuracy degradation.

“A UT neuromorphic computing center would provide the vertical integration of different technology readiness level from basic science to system applications that can significantly upgrade the human and machine interface,” Chen said.

The researchers on the team have a secret weapon at their disposal: the supercomputers at UT’s Texas Advanced Computing Center, including Frontera and Stampede2, the #1 and #2 most powerful supercomputers at any U.S. university.

“These systems allow us to predict the characteristics of materials, circuits, and devices before we construct them, and help us come up with the optimal designs for systems that can do things we’ve never done before,” he said.

Down in the Material Physics Lab, which Demkov co-leads, he shows off a machine made of steel cylinders and thick electrical wires capable of creating ultra-pure silicon photonic materials — the only one of its kind in the world. There he builds and tests nano-scale components invisible to the naked eye that could one day be ubiquitous, allowing computation to further embed itself in our day to day lives.

Despite competition from the world’s most powerful technology companies, Demkov believes UT has the resources and expertise to become the leader in neuromorphic computing.

“Most of the people in industry are working on this problem in a vacuum and are trying to gather ideas from their limited understanding of the literature,” he said. “We have the engine which drives the literature in our midst, and together we can create something truly extraordinary.”


Source:  Aaron Dubrow, Texas Advanced Computing Center, The University of Texas at Austin

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!

NSF Budget Approved for $8.3B in 2020, a 2.5% Increase

January 16, 2020

The National Science Foundation (NSF) has been spared a President Trump-proposed budget cut that would have rolled back its funding to 2012 levels. Congress passed legislation last month that sets the budget at $8.3 bill Read more…

By Staff report

NOAA Updates Its Massive, Supercomputer-Generated Climate Dataset

January 15, 2020

As Australia burns, understanding and mitigating the climate crisis is more urgent than ever. Now, by leveraging the computing resources at the National Energy Research Scientific Computing Center (NERSC), the U.S. National Oceanic and Atmospheric Administration (NOAA) has updated its 20th Century Reanalysis Project (20CR) dataset... Read more…

By Oliver Peckham

Atos-AMD System to Quintuple Supercomputing Power at European Centre for Medium-Range Weather Forecasts

January 15, 2020

The United Kingdom-based European Centre for Medium-Range Weather Forecasts (ECMWF), a supercomputer-powered weather forecasting organization backed by most of the countries in Europe, has signed a four-year, $89-million 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, the gold standard programming languages for fast performance Read more…

By John Russell

Quantum Computing, ML Drive 2019 Patent Awards

January 14, 2020

The dizzying pace of technology innovation often fueled by the growing availability of computing horsepower is underscored by the race to develop unique designs and application that can be patented. Among the goals of ma Read more…

By George Leopold

AWS Solution Channel

Challenging the barriers to High Performance Computing in the Cloud

Cloud computing helps democratize High Performance Computing by placing powerful computational capabilities in the hands of more researchers, engineers, and organizations who may lack access to sufficient on-premises infrastructure. Read more…

IBM Accelerated Insights

Intelligent HPC – Keeping Hard Work at Bay(es)

Since the dawn of time, humans have looked for ways to make their lives easier. Over the centuries human ingenuity has given us inventions such as the wheel and simple machines – which help greatly with tasks that would otherwise be extremely laborious. Read more…

Andrew Jones Joins Microsoft Azure HPC Team

January 13, 2020

Andrew Jones announced today he is joining Microsoft as part of the Azure HPC engineering & product team in early February. Jones makes the move after nearly 12 years at the UK HPC consultancy Numerical Algorithms Gr Read more…

By Staff report

Atos-AMD System to Quintuple Supercomputing Power at European Centre for Medium-Range Weather Forecasts

January 15, 2020

The United Kingdom-based European Centre for Medium-Range Weather Forecasts (ECMWF), a supercomputer-powered weather forecasting organization backed by most of 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

White House AI Regulatory Guidelines: ‘Remove Impediments to Private-sector AI Innovation’

January 9, 2020

When it comes to new technology, it’s been said government initially stays uninvolved – then gets too involved. The White House’s guidelines for federal a Read more…

By Doug Black

IBM Touts Quantum Network Growth, Improving QC Quality, and Battery Research

January 8, 2020

IBM today announced its Q (quantum) Network community had grown to 100-plus – Delta Airlines and Los Alamos National Laboratory are among most recent addition Read more…

By John Russell

HPCwire Awards Highlight Supercomputing Achievements in the Sciences

January 7, 2020

In November at SC19 in Denver, the HPCwire Readers’ and Editors’ Choice awards program celebrated its 16th year of honoring remarkable achievements in high-performance computing. With categories ranging from Best Use of HPC in Energy to Top HPC-Enabled Scientific Achievement, many of the winners contributed to groundbreaking developments in the sciences. This editorial highlights those awards. Read more…

By Oliver Peckham

Blasts from the (Recent) Past and Hopes for the Future

December 23, 2019

What does 2020 look like to you? What did 2019 look like? Lots happened but the main trends were carryovers from 2018 – AI messaging again blanketed everything; the roll-out of new big machines and exascale announcements continued; processor diversity and system disaggregation kicked up a notch; hyperscalers continued flexing their muscles (think AWS and its Graviton2 processor); and the U.S. and China continued their awkward trade war. Read more…

By John Russell

ARPA-E Applies ML to Power Generation Designs

December 19, 2019

The U.S. Energy Department’s research arm is leveraging machine learning technologies to simplify the design process for energy systems ranging from photovolt Read more…

By George Leopold

Focused on ‘Silicon TAM,’ Intel Puts Gary Patton, Former GlobalFoundries CTO, in Charge of Design Enablement

December 12, 2019

Change within Intel’s upper management – and to its company mission – has continued as a published report has disclosed that chip technology heavyweight G Read more…

By Doug Black

Using AI to Solve One of the Most Prevailing Problems in CFD

October 17, 2019

How can artificial intelligence (AI) and high-performance computing (HPC) solve mesh generation, one of the most commonly referenced problems in computational engineering? A new study has set out to answer this question and create an industry-first AI-mesh application... Read more…

By James Sharpe

D-Wave’s Path to 5000 Qubits; Google’s Quantum Supremacy Claim

September 24, 2019

On the heels of IBM’s quantum news last week come two more quantum items. D-Wave Systems today announced the name of its forthcoming 5000-qubit system, Advantage (yes the name choice isn’t serendipity), at its user conference being held this week in Newport, RI. Read more…

By John Russell

SC19: IBM Changes Its HPC-AI Game Plan

November 25, 2019

It’s probably fair to say IBM is known for big bets. Summit supercomputer – a big win. Red Hat acquisition – looking like a big win. OpenPOWER and Power processors – jury’s out? At SC19, long-time IBMer Dave Turek sketched out a different kind of bet for Big Blue – a small ball strategy, if you’ll forgive the baseball analogy... Read more…

By John Russell

Cray, Fujitsu Both Bringing Fujitsu A64FX-based Supercomputers to Market in 2020

November 12, 2019

The number of top-tier HPC systems makers has shrunk due to a steady march of M&A activity, but there is increased diversity and choice of processing compon Read more…

By Tiffany Trader

Crystal Ball Gazing: IBM’s Vision for the Future of Computing

October 14, 2019

Dario Gil, IBM’s relatively new director of research, painted a intriguing portrait of the future of computing along with a rough idea of how IBM thinks we’ Read more…

By John Russell

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

Intel Debuts New GPU – Ponte Vecchio – and Outlines Aspirations for oneAPI

November 17, 2019

Intel today revealed a few more details about its forthcoming Xe line of GPUs – the top SKU is named Ponte Vecchio and will be used in Aurora, the first plann Read more…

By John Russell

Dell Ramps Up HPC Testing of AMD Rome Processors

October 21, 2019

Dell Technologies is wading deeper into the AMD-based systems market with a growing evaluation program for the latest Epyc (Rome) microprocessors from AMD. In a Read more…

By John Russell

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

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

SC19: Welcome to Denver

November 17, 2019

A significant swath of the HPC community has come to Denver for SC19, which began today (Sunday) with a rich technical program. As is customary, the ribbon cutt Read more…

By Tiffany Trader

With the Help of HPC, Astronomers Prepare to Deflect a Real Asteroid

September 26, 2019

For years, NASA has been running simulations of asteroid impacts to understand the risks (and likelihoods) of asteroids colliding with Earth. Now, NASA and the European Space Agency (ESA) are preparing for the next, crucial step in planetary defense against asteroid impacts: physically deflecting a real asteroid. Read more…

By Oliver Peckham

Jensen Huang’s SC19 – Fast Cars, a Strong Arm, and Aiming for the Cloud(s)

November 20, 2019

We’ve come to expect Nvidia CEO Jensen Huang’s annual SC keynote to contain stunning graphics and lively bravado (with plenty of examples) in support of GPU Read more…

By John Russell

Top500: US Maintains Performance Lead; Arm Tops Green500

November 18, 2019

The 54th Top500, revealed today at SC19, is a familiar list: the U.S. Summit (ORNL) and Sierra (LLNL) machines, offering 148.6 and 94.6 petaflops respectively, Read more…

By Tiffany Trader

51,000 Cloud GPUs Converge to Power Neutrino Discovery at the South Pole

November 22, 2019

At the dead center of the South Pole, thousands of sensors spanning a cubic kilometer are buried thousands of meters beneath the ice. The sensors are part of Ic Read more…

By Oliver Peckham

Azure Cloud First with AMD Epyc Rome Processors

November 6, 2019

At Ignite 2019 this week, Microsoft's Azure cloud team and AMD announced an expansion of their partnership that began in 2017 when Azure debuted Epyc-backed instances for storage workloads. The fourth-generation Azure D-series and E-series virtual machines previewed at the Rome launch in August are now generally available. Read more…

By Tiffany Trader

Kubernetes, Containers and HPC

September 19, 2019

Software containers and Kubernetes are important tools for building, deploying, running and managing modern enterprise applications at scale and delivering enterprise software faster and more reliably to the end user — while using resources more efficiently and reducing costs. Read more…

By Daniel Gruber, Burak Yenier and Wolfgang Gentzsch, UberCloud

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