Fredrick Rothganger

Sandia National Laboratories

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James B. Aimone

Sandia National Laboratories

Stephen Verzi

Sandia National Laboratories

Craig M. Vineyard

Sandia National Laboratories

Conrad D. James

Sandia National Laboratories California

Christina E. Warrender

Sandia National Laboratories

William Severa

Sandia National Laboratories

Asmeret Naugle

Sandia National Laboratories

Felix Wang

Sandia National Laboratories

Suma Cardwell

Sandia National Laboratories

John H. Naegle

Sandia National Laboratories

Cooperative Institutions

Sandia National Laboratories California

Sandia National Laboratories

Office of Scientific and Technical Information

National Technical Information Service

University of New Mexico

University of Illinois Urbana-Champaign

Oak Ridge National Laboratory

Georgia Institute of Technology

Institute of Electrical and Electronics Engineers

Cornell University

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Trust in Influence Dynamics: Using Reinforcement Learning to Evaluate Defense Strategies.

Asmeret Naugle Asael Sorensen Fredrick Rothganger Stephen Verzi cas doyle

Dynamics

10.2172/2003961

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Modeling Analog Tile-Based Accelerators Using SST

Benjamin Feinberg Sapan Agarwal Mark Plagge Fredrick Rothganger Suma Cardwell

Analog computing has been widely proposed to improve the energy ef?ciency of multiple important workloads including neural network operations, and other linear algebra kernels. To properly evaluate analog computing and explore more complex workloads such as systems consisting of multiple analog data paths, system level simulations are required. Moreover, prior work on system architectures for analog computing often rely on custom simulators creating signi?cant additional design effort and complicating comparisons between different systems. To remedy these issues, this report describes the design and implementation of a ?exible tile-based analog accelerator element for the Structural Simulation Toolkit (SST). The element focuses on heavily on the tile controller?an often neglected aspect of prior work?that is suf?ciently versatile to simulate a wide range of different tile operations including neural network layers, signal processing kernels, and generic linear algebra operations without major constraints. The tile model also interoperates with existing SST memory and network models to reduce the overall development load and enable future simulation of heterogeneous systems with both conventional digital logic and analog compute tiles. Finally, both the tile and array models are designed to easily support future extensions as new analog operations and applications that can bene?t from analog computing are developed.

Analog computer

10.2172/1891950

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How to model the whole brain

Fredrick Rothganger Derek Trumbo Christina Warrender James B. Aimone

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Behavioral Feedback in Disease Dynamics.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2021)

Fredrick Rothganger Asmeret Naugle Stephen Verzi

Dynamics

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ATHNEA: Enabling Codesign for Next-Generation AI/ML Architectures .

Mark Plagge Benjamin Feinberg John McFarland Fredrick Rothganger Sapan Agarwal

10.2172/2006315

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Training neural hardware with noisy components.

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2015)

Fredrick Rothganger Brian R. Evans James B. Aimone Erik P. DeBenedictis

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N2A: a computational tool for modeling from neurons to algorithms

Frontiers in Neural Circuits (2014)

Fredrick Rothganger Christina Warrender Derek Trumbo James B. Aimone

The exponential increase in available neural data has combined with the exponential growth in computing ("Moore's law") to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains. Furthermore, the size and detail of models have grown to levels for which understanding the implications of variability and assumptions is no longer trivial. Here, we introduce the model design platform N2A which aims to facilitate the design and validation of biologically realistic models. N2A uses a hierarchical representation of neural information to enable the integration of models from different users. N2A streamlines computational validation of a model by natively implementing standard tools in sensitivity analysis and uncertainty quantification. The part-relationship representation allows both network-level analysis and dynamical simulations. We will demonstrate how N2A can be used in a range of examples, including a simple Hodgkin-Huxley cable model, basic parameter sensitivity of an 80/20 network, and the expression of the structural plasticity of a growing dendrite and stem cell proliferation and differentiation.

Computational neuroscience

10.3389/fncir.2014.00001

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Group formation theory at multiple scales.

Casey Doyle Asmeret Naugle Michael L. Bernard Kiran Lakkaraju Robert Kittinger

10.2172/1831039

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Neural circuit models on emulated hardware.

Fredrick Rothganger David R. Follett John H. Naegle Felix Wang Jonathon W. Donaldson

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Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform

Craig M. Vineyard Ryan Dellana James B. Aimone Fredrick Rothganger William Severa

With the successes deep neural networks have achieved across a range of applications, researchers have been exploring computational architectures to more efficiently execute their operation. In addition to the prevalent role of graphics processing units (GPUs), many accelerator architectures have emerged. Neuromorphic is one such particular approach which takes inspiration from the brain to guide the computational principles of the architecture including varying levels of biological realism. In this paper we present results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia National Laboratories developed Whetstone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.

Neuromorphic engineering

Perceptron

10.1145/3320288.3320300

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Citations (7)