A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers

Potok, Thomas E.; Schuman, Catherine; Young, Steven; Patton, Robert; Spedalieri, Federico; Liu, Jeremy; Yao, Ke-Thia; Rose, Garrett; Chakma, Gangotree

doi:10.1145/3178454

Title: A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers

Abstract

Current deep learning approaches have been very successful using convolutional neural networks trained on large graphical-processing-unit-based computers. Three limitations of this approach are that (1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections; (2) the networks are manually configured to achieve optimal results, and (3) the implementation of the network model is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. We use the MNIST dataset for our experiment, due to input size limitations of current quantum computers. Our results show the feasibility of using the three architectures in tandem to address the above deep learning limitations. Finally, we show that a quantum computer can find high quality values of intra-layer connection weights in a tractable time as the complexity of the network increases, a high performance computer can find optimal layer-based topologies, and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.

Authors:

Potok, Thomas E. ^[1]; Schuman, Catherine ^[1]; Young, Steven ^[1]; Patton, Robert ^[1]; Spedalieri, Federico ^[2]; Liu, Jeremy ^[2]; Yao, Ke-Thia ^[2]; Rose, Garrett ^[3]; Chakma, Gangotree ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Data Analytics Group
Univ. of Southern California, Marina del Rey, CA (United States). Information Sciences Inst.
Univ. of Tennessee, Knoxville, TN (United States). Dept. of Electrical Engineering and Computer Science

Publication Date:: Fri Jul 27 00:00:00 EDT 2018

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1474723

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: ACM Journal on Emerging Technologies in Computing Systems

Additional Journal Information:: Journal Volume: 14; Journal Issue: 2; Journal ID: ISSN 1550-4832

Publisher:: Association for Computing Machinery

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; deep learning; quantum computing; neuromorphic computing; high-performance computing

Citation Formats


                    Potok, Thomas E., Schuman, Catherine, Young, Steven, Patton, Robert, Spedalieri, Federico, Liu, Jeremy, Yao, Ke-Thia, Rose, Garrett, and Chakma, Gangotree. A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers.  United States: N. p., 2018. 
Web.  doi:10.1145/3178454.

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                    Potok, Thomas E., Schuman, Catherine, Young, Steven, Patton, Robert, Spedalieri, Federico, Liu, Jeremy, Yao, Ke-Thia, Rose, Garrett, & Chakma, Gangotree. A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers.  United States.  https://doi.org/10.1145/3178454

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                    Potok, Thomas E., Schuman, Catherine, Young, Steven, Patton, Robert, Spedalieri, Federico, Liu, Jeremy, Yao, Ke-Thia, Rose, Garrett, and Chakma, Gangotree. Fri .  
"A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers".  United States.  https://doi.org/10.1145/3178454.  https://www.osti.gov/servlets/purl/1474723.

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@article{osti_1474723,

  title        = {A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers},

  author       = {Potok, Thomas E. and Schuman, Catherine and Young, Steven and Patton, Robert and Spedalieri, Federico and Liu, Jeremy and Yao, Ke-Thia and Rose, Garrett and Chakma, Gangotree},

  abstractNote = {Current deep learning approaches have been very successful using convolutional neural networks trained on large graphical-processing-unit-based computers. Three limitations of this approach are that (1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections; (2) the networks are manually configured to achieve optimal results, and (3) the implementation of the network model is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. We use the MNIST dataset for our experiment, due to input size limitations of current quantum computers. Our results show the feasibility of using the three architectures in tandem to address the above deep learning limitations. Finally, we show that a quantum computer can find high quality values of intra-layer connection weights in a tractable time as the complexity of the network increases, a high performance computer can find optimal layer-based topologies, and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware.},

  doi          = {10.1145/3178454},

  journal      = {ACM Journal on Emerging Technologies in Computing Systems},

  number       = 2,

  volume       = 14,

  place        = {United States},

  year         = {Fri Jul 27 00:00:00 EDT 2018},

  month        = {Fri Jul 27 00:00:00 EDT 2018}

}

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Similar Records

Title: A Study of Complex Deep Learning Networks on High-Performance, Neuromorphic, and Quantum Computers

Abstract

Citation Formats

A Learning Algorithm for Boltzmann Machines* journal, January 1985

What is the best multi-stage architecture for object recognition? conference, September 2009

High switching endurance in TaOx memristive devices journal, December 2010

Harmonica: A Framework of Heterogeneous Computing Systems With Memristor-Based Neuromorphic Computing Accelerators journal, May 2016

Neuromorphic architectures for spiking deep neural networks conference, December 2015

Spatiotemporal Classification Using Neuroscience-Inspired Dynamic Architectures journal, January 2014

Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores conference, August 2013

Quantum annealing with manufactured spins journal, May 2011

Training and operation of an integrated neuromorphic network based on metal-oxide memristors journal, May 2015

Extending SpikeProp conference, January 2004

Simulating physics with computers journal, June 1982

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing conference, July 2015

ImageNet Large Scale Visual Recognition Challenge journal, April 2015

Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks journal, December 2015

Nanoscale Memristor Device as Synapse in Neuromorphic Systems journal, April 2010

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification conference, December 2015

A Fast Learning Algorithm for Deep Belief Nets journal, July 2006

Error-backpropagation in temporally encoded networks of spiking neurons journal, October 2002

A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications journal, December 2011

Reducing the Dimensionality of Data with Neural Networks journal, July 2006

DANNA: A neuromorphic software ecosystem journal, July 2016

Circuit Techniques for Online Learning of Memristive Synapses in CMOS-Memristor Neuromorphic Systems conference, January 2017

Optimizing deep learning hyper-parameters through an evolutionary algorithm conference, January 2015

FaceNet: A unified embedding for face recognition and clustering conference, June 2015

How We Found The Missing Memristor journal, December 2008

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning journal, August 2016

Building block of a programmable neuromorphic substrate: A digital neurosynaptic core conference, June 2012

A Memristor Crossbar Based Computing Engine Optimized for High Speed and Accuracy conference, July 2016

Long Short-Term Memory journal, November 1997

Gradient-based learning applied to document recognition journal, January 1998

Lognormal switching times for titanium dioxide bipolar memristors: origin and resolution journal, January 2011

Competitive Hebbian learning through spike-timing-dependent synaptic plasticity journal, September 2000

Darwin: A neuromorphic hardware co-processor based on spiking neural networks journal, June 2017

ImageNet Large Scale Visual Recognition Challenge text, January 2015

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing text, January 2015

Neuromorphic Architectures for Spiking Deep Neural Networks text, January 2015

Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors text, January 2014

Performance analysis and optimization for scalable deployment of deep learning models for country‐scale settlement mapping on Titan supercomputer journal, May 2019

Quantum machine learning with D‐wave quantum computer journal, June 2019

Reliability of analog resistive switching memory for neuromorphic computing journal, March 2020

Intelligent video surveillance: a review through deep learning techniques for crowd analysis journal, June 2019

A Learning Algorithm for Boltzmann Machines*
journal, January 1985

What is the best multi-stage architecture for object recognition?
conference, September 2009

High switching endurance in TaOx memristive devices
journal, December 2010

Harmonica: A Framework of Heterogeneous Computing Systems With Memristor-Based Neuromorphic Computing Accelerators
journal, May 2016

Neuromorphic architectures for spiking deep neural networks
conference, December 2015

Spatiotemporal Classification Using Neuroscience-Inspired Dynamic Architectures
journal, January 2014

Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores
conference, August 2013

Quantum annealing with manufactured spins
journal, May 2011

Training and operation of an integrated neuromorphic network based on metal-oxide memristors
journal, May 2015

Extending SpikeProp
conference, January 2004

Simulating physics with computers
journal, June 1982

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
conference, July 2015

ImageNet Large Scale Visual Recognition Challenge
journal, April 2015

Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks
journal, December 2015

Nanoscale Memristor Device as Synapse in Neuromorphic Systems
journal, April 2010

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
conference, December 2015

A Fast Learning Algorithm for Deep Belief Nets
journal, July 2006

Error-backpropagation in temporally encoded networks of spiking neurons
journal, October 2002

A Functional Hybrid Memristor Crossbar-Array/CMOS System for Data Storage and Neuromorphic Applications
journal, December 2011

Reducing the Dimensionality of Data with Neural Networks
journal, July 2006

DANNA: A neuromorphic software ecosystem
journal, July 2016

Circuit Techniques for Online Learning of Memristive Synapses in CMOS-Memristor Neuromorphic Systems
conference, January 2017

Optimizing deep learning hyper-parameters through an evolutionary algorithm
conference, January 2015

FaceNet: A unified embedding for face recognition and clustering
conference, June 2015

How We Found The Missing Memristor
journal, December 2008

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
journal, August 2016

Building block of a programmable neuromorphic substrate: A digital neurosynaptic core
conference, June 2012

A Memristor Crossbar Based Computing Engine Optimized for High Speed and Accuracy
conference, July 2016

Long Short-Term Memory
journal, November 1997

Gradient-based learning applied to document recognition
journal, January 1998

Lognormal switching times for titanium dioxide bipolar memristors: origin and resolution
journal, January 2011

Competitive Hebbian learning through spike-timing-dependent synaptic plasticity
journal, September 2000

Darwin: A neuromorphic hardware co-processor based on spiking neural networks
journal, June 2017

ImageNet Large Scale Visual Recognition Challenge
text, January 2015

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
text, January 2015

Neuromorphic Architectures for Spiking Deep Neural Networks
text, January 2015

Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
text, January 2014

Performance analysis and optimization for scalable deployment of deep learning models for country‐scale settlement mapping on Titan supercomputer
journal, May 2019

Quantum machine learning with D‐wave quantum computer
journal, June 2019

Reliability of analog resistive switching memory for neuromorphic computing
journal, March 2020

Intelligent video surveillance: a review through deep learning techniques for crowd analysis
journal, June 2019