A Programming Framework for Neuromorphic Systems with Emerging Technologies

Schuman, Catherine; Plank, James; Rose, Garrett; Chakma, Gangotree; Wyer, Austin; Bruer, Grant; Laanait, Nouamane

doi:10.1145/3109453.3123958

Title: A Programming Framework for Neuromorphic Systems with Emerging Technologies

Abstract

Neuromorphic computing is a promising post-Moore's law era technology. A wide variety of neuromorphic computer (NC) architectures have emerged in recent years, ranging from traditional fully digital CMOS to nanoscale implementations with novel, beyond CMOS components. There are already major questions associated with how we are going to program and use NCs simply because of how radically different their architecture is as compared with the von Neumann architecture. When coupled with the implementations using emerging device technologies, which add additional issues associated with programming devices, it is clear that we must define a new way to program and develop for NC devices. In this work, we discuss a programming framework for NC devices implemented with emerging technologies. We discuss how we have applied this framework to program a NC system implemented with metal oxide memristors. We utilize the framework to develop two applications for the memristive NC device: a simple multiplexer and a simple control task (the cart-pole problem). Finally, we discuss how this framework can be extended to NC systems implemented with a variety of novel device components and materials.

Authors:

^[1]; Plank, James ^[2]; Rose, Garrett ^[2]; Chakma, Gangotree ^[2]; Wyer, Austin ^[3]; Bruer, Grant ^[2];

^[1]

ORNL
University of Tennessee (UT)
University of Tennessee, Knoxville (UTK)

Publication Date:: Sun Oct 01 00:00:00 EDT 2017

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1407781

DOE Contract Number:: AC05-00OR22725

Resource Type:: Conference

Resource Relation:: Conference: 4th ACM International Conference on Nanoscale Computing and Communication - Washington D.C., Virginia, United States of America - 9/27/2017 8:00:00 AM-9/29/2017 8:00:00 AM

Country of Publication:: United States

Language:: English

Citation Formats


                    Schuman, Catherine, Plank, James, Rose, Garrett, Chakma, Gangotree, Wyer, Austin, Bruer, Grant, and Laanait, Nouamane. A Programming Framework for Neuromorphic Systems with Emerging Technologies.  United States: N. p., 2017. 
        Web.  doi:10.1145/3109453.3123958.

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                    Schuman, Catherine, Plank, James, Rose, Garrett, Chakma, Gangotree, Wyer, Austin, Bruer, Grant, & Laanait, Nouamane. A Programming Framework for Neuromorphic Systems with Emerging Technologies.  United States.  https://doi.org/10.1145/3109453.3123958

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                    Schuman, Catherine, Plank, James, Rose, Garrett, Chakma, Gangotree, Wyer, Austin, Bruer, Grant, and Laanait, Nouamane. 2017.  
        "A Programming Framework for Neuromorphic Systems with Emerging Technologies".  United States.  https://doi.org/10.1145/3109453.3123958.  https://www.osti.gov/servlets/purl/1407781.

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@article{osti_1407781,

  title        = {A Programming Framework for Neuromorphic Systems with Emerging Technologies},

  author       = {Schuman, Catherine and Plank, James and Rose, Garrett and Chakma, Gangotree and Wyer, Austin and Bruer, Grant and Laanait, Nouamane},

  abstractNote = {Neuromorphic computing is a promising post-Moore's law era technology. A wide variety of neuromorphic computer (NC) architectures have emerged in recent years, ranging from traditional fully digital CMOS to nanoscale implementations with novel, beyond CMOS components. There are already major questions associated with how we are going to program and use NCs simply because of how radically different their architecture is as compared with the von Neumann architecture. When coupled with the implementations using emerging device technologies, which add additional issues associated with programming devices, it is clear that we must define a new way to program and develop for NC devices. In this work, we discuss a programming framework for NC devices implemented with emerging technologies. We discuss how we have applied this framework to program a NC system implemented with metal oxide memristors. We utilize the framework to develop two applications for the memristive NC device: a simple multiplexer and a simple control task (the cart-pole problem). Finally, we discuss how this framework can be extended to NC systems implemented with a variety of novel device components and materials.},

  doi          = {10.1145/3109453.3123958},

  url          = {https://www.osti.gov/biblio/1407781},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Oct 01 00:00:00 EDT 2017},

  month        = {Sun Oct 01 00:00:00 EDT 2017}

}

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Title: A Programming Framework for Neuromorphic Systems with Emerging Technologies

Abstract

Citation Formats

A neuromorphic circuit mimicking biological short-term memory conference, August 2016

Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations journal, May 2014

Band-Selective Measurements of Electron Dynamics in VO 2 Using Femtosecond Near-Edge X-Ray Absorption journal, August 2005

Neural identification of dynamic systems on FPGA with improved PSO learning journal, September 2012

An Application Development Platform for neuromorphic computing conference, July 2016

Convolutional networks for fast, energy-efficient neuromorphic computing journal, September 2016

Overview of the SpiNNaker System Architecture journal, December 2013

Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates journal, December 2002

Spintronic Nanodevices for Bioinspired Computing journal, October 2016

Evolving Unipolar Memristor Spiking Neural Networks book, January 2015

Magnetic skyrmion-based synaptic devices journal, January 2017

Resistive Switching in Mott Insulators and Correlated Systems journal, July 2015

Critical Behavior of the Mott Transition in Cr-Doped V 2 O 3 journal, November 1970

Band excitation in scanning probe microscopy: sines of change journal, November 2011

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

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses journal, September 2016

Challenges for large-scale implementations of spiking neural networks on FPGAs journal, December 2007

Demonstration of spike timing dependent plasticity in CBRAM devices with silicon neurons conference, May 2016

Energy-Efficient Neuromorphic Classifiers journal, October 2016

A million spiking-neuron integrated circuit with a scalable communication network and interface journal, August 2014

Neuromorphic computing gets ready for the (really) big time journal, June 2014

Learning to be efficient conference, April 2016

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

Resistive switching in transition metal oxides journal, June 2008

Extending SpikeProp conference, January 2004

Dynamic Artificial Neural Networks with Affective Systems journal, November 2013

Neuroscience-inspired inspired dynamic architectures conference, May 2014

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

Parallel Evolutionary Optimization for Neuromorphic Network Training conference, November 2016

An evolutionary optimization framework for neural networks and neuromorphic architectures conference, July 2016

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

Synaptic plasticity and memristive behavior operated by atomic switches conference, July 2014

Electric-Pulse-driven Electronic Phase Separation, Insulator-Metal Transition, and Possible Superconductivity in a Mott Insulator journal, July 2008

Electric-pulse-induced resistive switching and possible superconductivity in the Mott insulator GaTa4Se8 journal, December 2008

Controllable Multiple Depression in a Graphene Oxide Artificial Synapse journal, November 2016

Synaptic plasticity and learning behaviours in flexible artificial synapse based on polymer/viologen system journal, January 2016

A neuromorphic circuit mimicking biological short-term memory
conference, August 2016

Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
journal, May 2014

Band-Selective Measurements of Electron Dynamics in ${VO}_{2}$ Using Femtosecond Near-Edge X-Ray Absorption
journal, August 2005

Neural identification of dynamic systems on FPGA with improved PSO learning
journal, September 2012

An Application Development Platform for neuromorphic computing
conference, July 2016

Convolutional networks for fast, energy-efficient neuromorphic computing
journal, September 2016

Overview of the SpiNNaker System Architecture
journal, December 2013

Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates
journal, December 2002

Spintronic Nanodevices for Bioinspired Computing
journal, October 2016

Evolving Unipolar Memristor Spiking Neural Networks
book, January 2015

Magnetic skyrmion-based synaptic devices
journal, January 2017

Resistive Switching in Mott Insulators and Correlated Systems
journal, July 2015

Critical Behavior of the Mott Transition in Cr-Doped $V_{2}$ $O_{3}$
journal, November 1970

Band excitation in scanning probe microscopy: sines of change
journal, November 2011

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

Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses
journal, September 2016

Challenges for large-scale implementations of spiking neural networks on FPGAs
journal, December 2007

Demonstration of spike timing dependent plasticity in CBRAM devices with silicon neurons
conference, May 2016

Energy-Efficient Neuromorphic Classifiers
journal, October 2016

A million spiking-neuron integrated circuit with a scalable communication network and interface
journal, August 2014

Neuromorphic computing gets ready for the (really) big time
journal, June 2014

Learning to be efficient
conference, April 2016

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

Resistive switching in transition metal oxides
journal, June 2008

Extending SpikeProp
conference, January 2004

Dynamic Artificial Neural Networks with Affective Systems
journal, November 2013

Neuroscience-inspired inspired dynamic architectures
conference, May 2014

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

Parallel Evolutionary Optimization for Neuromorphic Network Training
conference, November 2016

An evolutionary optimization framework for neural networks and neuromorphic architectures
conference, July 2016

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

Synaptic plasticity and memristive behavior operated by atomic switches
conference, July 2014

Electric-Pulse-driven Electronic Phase Separation, Insulator-Metal Transition, and Possible Superconductivity in a Mott Insulator
journal, July 2008

Electric-pulse-induced resistive switching and possible superconductivity in the Mott insulator GaTa4Se8
journal, December 2008

Controllable Multiple Depression in a Graphene Oxide Artificial Synapse
journal, November 2016

Synaptic plasticity and learning behaviours in flexible artificial synapse based on polymer/viologen system
journal, January 2016