Publications

Parsa, Maryam P.; Schuman, Catherine D.; Date, Prasanna D.; Rose, Derek R.; Kay, Bill K.; Mitchell, J.P.; Young, Steven Y.; Dellana, Ryan A.; Severa, William M.; Potok, Thomas P.; Roy, Kaushik R.

Abstract not provided.

Bennett, Christopher H.; Dellana, Ryan A.; Xiao, Tianyao X.; Feinberg, Benjamin F.; Agarwal, Sapan A.; Cardwell, Suma G.; Marinella, Matthew J.; Severa, William M.; Aimone, James B.

Abstract not provided.

Smith, John D.; Aimone, James B.; Severa, William M.

Abstract not provided.

Smith, John D.; Aimone, James B.; Franke, Brian C.; Hill, Aaron J.; Lehoucq, Richard B.; Parekh, Ojas D.; Reeder, Leah E.; Severa, William M.

Abstract not provided.

Severa, William M.

Abstract not provided.

Moen, Christopher D.; Li, Zhiyong L.; Severa, William M.

Abstract not provided.

Wang, Felix W.; Severa, William M.; Aimone, James B.

Abstract not provided.

Musuvathy, Srideep M.; Aimone, James B.; Cardwell, Suma G.; Chance, Frances S.; Dellana, Ryan A.; Ho, Yang H.; Reeder, Leah E.; Severa, William M.; Vineyard, Craig M.; Wang, Felix W.

Abstract not provided.

Cardwell, Suma G.; Aimone, James B.; Bennett, Christopher H.; Dellana, Ryan A.; Cardwell, Suma G.; Severa, William M.

Abstract not provided.

Vineyard, Craig M.; Severa, William M.; Green, Sam G.; Dellana, Ryan A.; Plagge, Mark P.; Hill, Aaron J.

Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.

Aimone, James B.; Parekh, Ojas D.; Phillips, Cynthia A.; Pinar, Ali P.; Severa, William M.; Xu, Helen

Abstract not provided.

Wang, Felix W.; Green, Sam G.; Severa, William M.

Abstract not provided.

Wang, Felix W.; Green, Sam G.; Severa, William M.

Abstract not provided.

ACM International Conference Proceeding Series

Aimone, James B.; Severa, William M.; Vineyard, Craig M.

Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, these computer platforms can offer significant energy benefits compared to traditional von Neumann processors. Unfortunately there still remains considerable difficulty in successfully programming, configuring and deploying neuromorphic systems. We present the Fugu framework as an answer to this need. Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources. Individual kernels linked together provide sophisticated processing through compositionality. Fugu is intended to be suitable for a wide-range of neuromorphic applications, including machine learning, scientific computing, and more brain-inspired neural algorithms. Ultimately, we hope the community adopts this and other open standardization attempts allowing for free exchange and easy implementations of the ever-growing list of spiking neural algorithms.

ACM International Conference Proceeding Series

Aimone, James B.; Pinar, Ali P.; Parekh, Ojas D.; Severa, William M.; Phillips, Cynthia A.; Xu, Helen

With the advent of large-scale neuromorphic platforms, we seek to better understand the applications of neuromorphic computing to more general-purpose computing domains. Graph analysis problems have grown increasingly relevant in the wake of readily available massive data. We demonstrate that a broad class of combinatorial and graph problems known as dynamic programs enjoy simple and efficient neuromorphic implementations, by developing a general technique to convert dynamic programs to spiking neuromorphic algorithms. Dynamic programs have been studied for over 50 years and have dozens of applications across many fields.

Vineyard, Craig M.; Green, Sam G.; Severa, William M.; Koc, Cetin K.

Abstract not provided.

Severa, William M.

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Proceedings - 2019 IEEE Space Computing Conference, SCC 2019

Vineyard, Craig M.; Severa, William M.; Kagie, Matthew J.; Scholand, Andrew J.; Hays, Park H.

Technological advances have enabled exponential growth in both sensor data collection, as well as computational processing. However, as a limiting factor, the transmission bandwidth in between a space-based sensor and a ground station processing center has not seen the same growth. A resolution to this bandwidth limitation is to move the processing to the sensor, but doing so faces size, weight, and power operational constraints. Different physical constraints on processor manufacturing are spurring a resurgence in neuromorphic approaches amenable to the space-based operational environment. Here we describe historical trends in computer architecture and the implications for neuromorphic computing, as well as give an overview of how remote sensing applications may be impacted by this emerging direction for computing.

Proceedings - 2019 IEEE Space Computing Conference, SCC 2019

Yanguas-Gil, Angel; Mane, Anil; Elam, Jeffrey W.; Wang, Felix W.; Severa, William M.; Daram, Anurag R.; Kudithipudi, Dhireesha

The insect brain is a great model system for low power electronics: Insects carry out multisensory integration and are able to change the way the process information, learn, and adapt to changes in their environment with a very limited power budget. This context-dependent processing allows them to implement multiple functionalities within the same network, as well as to minimize power consumption by having context-dependent gains in their first layers of input processing. The combination of low power consumption, adaptability and online learning, and robustness makes them particularly appealing for a number of space applications, from rovers and probes to satellites, all having to deal with the progressive degradation of their capabilities in remote environments. In this work, we explore architectures inspired in the insect brain capable of context-dependent processing and learning. Starting from algorithms, we have explored three different implementations: A spiking implementation in a neuromorphic chip, a custom implementation in an FPGA, and finally hybrid analog/digital implementations based on cross-bar arrays. For the latter, we found that the development of novel resistive materials is crucial in order to enhance the energy efficiency of analog devices while maintaining an adequate footprint. Metal-oxide nanocomposite materials, fabricated using ALD with processes compatible with semiconductor processing, are promising candidates to fill in that role.

Hill, Aaron J.; Donaldson, Jonathon W.; Rothganger, Fredrick H.; Vineyard, Craig M.; Follett, David R.; Follett, Pamela L.; Smith, Michael R.; Verzi, Stephen J.; Severa, William M.; Wang, Felix W.; Aimone, James B.; Naegle, John H.; James, Conrad D.

Abstract not provided.

ACM International Conference Proceeding Series

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

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.

Hill, Aaron J.; Severa, William M.; Vineyard, Craig M.; Dellana, Ryan A.; Reeder, Leah E.; Wang, Felix W.; Aimone, James B.; Yanguas-Gil, Angel Y.

Abstract not provided.

Reeder, Leah E.; Aimone, James B.; Severa, William M.; Hill, Aaron J.

Abstract not provided.

Wang, Felix W.; Severa, William M.; Rothganger, Fredrick R.

Abstract not provided.

Perego, Mauro P.; Jakeman, John D.; Severa, William M.; Ruthotto, Lars R.

Abstract not provided.