Publications

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.

Proceedings - 2017 International Conference on Computational Science and Computational Intelligence, CSCI 2017

Doak, Justin E.; Ingram, Joey; Mulder, Samuel A.; Naegle, John H.; Cox, Jonathan A.; Aimone, James B.; Dixon, Kevin R.; James, Conrad D.; Follett, David R.

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expression (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities.In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naïve solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naïve solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.

2017 IEEE International Conference on Rebooting Computing, ICRC 2017 - Proceedings

Hill, Aaron J.; Donaldson, Jonathon W.; Rothganger, Fredrick R.; 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.

Unlike general purpose computer architectures that are comprised of complex processor cores and sequential computation, the brain is innately parallel and contains highly complex connections between computational units (neurons). Key to the architecture of the brain is a functionality enabled by the combined effect of spiking communication and sparse connectivity with unique variable efficacies and temporal latencies. Utilizing these neuroscience principles, we have developed the Spiking Temporal Processing Unit (STPU) architecture which is well-suited for areas such as pattern recognition and natural language processing. In this paper, we formally describe the STPU, implement the STPU on a field programmable gate array, and show measured performance data.

Doak, Justin E.; Doak, Justin E.; Ingram, Joey; Ingram, Joey; Mulder, Samuel A.; Mulder, Samuel A.; Naegle, John H.; Naegle, John H.; Cox, Jonathan A.; Cox, Jonathan A.; Aimone, James B.; Aimone, James B.; Dixon, Kevin R.; Dixon, Kevin R.; James, Conrad D.; James, Conrad D.; Follet, David R.; Follet, David R.

Abstract not provided.

ACM International Conference Proceeding Series

Follett, David R.; Karpman, Gabe D.; Townsend, Duncan; Naegle, John H.; Follett, Pamela L.; Suppona, Roger A.; Aimone, James B.; James, Conrad D.

In 2016, Lewis Rhodes Labs, (LRL), shipped the first commercially viable Neuromorphic Processing Unit, (NPU), branded as a Neuromorphic Data Microscope (NDM). This product leverages architectural mechanisms derived from the sensory cortex of the human brain to efficiently implement pattern matching. LRL and Sandia National Labs have optimized this product for streaming analytics, and demonstrated a 1,000x power per operation reduction in an FPGA format. When reduced to an ASIC, the efficiency will improve to 1,000,000x. Additionally, the neuromorphic nature of the device gives it powerful computational attributes that are counterintuitive to those schooled in traditional von Neumann architectures. The Neuromorphic Data Microscope is the first of a broad class of brain-inspired, time domain processors that will profoundly alter the functionality and economics of data processing.

Proceedings of the International Joint Conference on Neural Networks

Smith, Michael R.; Hill, Aaron J.; Carlson, Kristofor D.; Vineyard, Craig M.; Donaldson, Jonathon W.; Follett, David R.; Follett, Pamela L.; Naegle, John H.; James, Conrad D.; Aimone, James B.

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU - demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.

Smith, Michael R.; Hill, Aaron J.; Carlson, Kristofor D.; Vineyard, Craig M.; Donaldson, Jonathon W.; Follett, David R.; Follett, Pamela L.; Naegle, John H.; James, Conrad D.; Aimone, James B.; Smith, Michael R.; Smith, Michael R.

Abstract not provided.

Smith, Michael R.; Hill, Aaron J.; Carlson, Kristofor D.; Vineyard, Craig M.; Donaldson, Jonathon W.; Follett, David R.; Follett, Pamela L.; Naegle, John H.; James, Conrad D.; Aimone, James B.

Abstract not provided.

Biologically Inspired Cognitive Architectures

James, Conrad D.; Aimone, James B.; Miner, Nadine E.; Vineyard, Craig M.; Rothganger, Fredrick R.; Carlson, Kristofor D.; Mulder, Samuel A.; Draelos, Timothy J.; Faust, Aleksandra; Marinella, Matthew J.; Naegle, John H.; Plimpton, Steven J.

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

Vineyard, Craig M.; Aimone, James B.; Verzi, Stephen J.; Donaldson, Jonathon W.; Smith, Michael R.; Rothganger, Fredrick R.; Follet, David R.; James, Conrad D.; Naegle, John H.

Abstract not provided.

Smith, Michael R.; Hill, Aaron J.; Carlson, Kristofor D.; Vineyard, Craig M.; Donaldson, Jonathon W.; Follett, David R.; Follett, Pamela L.; Naegle, John H.; James, Conrad D.; Aimone, James B.

Abstract not provided.

Rothganger, Fredrick R.; Follett, David R.; Naegle, John H.; Wang, Felix W.; Donaldson, Jonathon W.; Vineyard, Craig M.; James, Conrad D.; Aimone, James B.

Abstract not provided.

James, Conrad D.; Aimone, James B.; Plimpton, Steven J.; Naegle, John H.; Marinella, Matthew J.; Dixon, Kevin R.; Follett, David R.

Abstract not provided.

Hemmert, Karl S.; Naegle, John H.

Abstract not provided.

Onunkwo, Uzoma O.; Johnson, Joshua A.; Naegle, John H.; Patel, Jay D.; Pearson, David B.; Shelburg, Jeffery S.; Wheeler, Kyle B.; Wright, Brian J.; Zage, David J.

This report documents our first year efforts to address the use of many-core processors for high performance cyber protection. As the demands grow for higher bandwidth (beyond 1 Gbits/sec) on network connections, the need to provide faster and more efficient solution to cyber security grows. Fortunately, in recent years, the development of many-core network processors have seen increased interest. Prior working experiences with many-core processors have led us to investigate its effectiveness for cyber protection tools, with particular emphasis on high performance firewalls. Although advanced algorithms for smarter cyber protection of high-speed network traffic are being developed, these advanced analysis techniques require significantly more computational capabilities than static techniques. Moreover, many locations where cyber protections are deployed have limited power, space and cooling resources. This makes the use of traditionally large computing systems impractical for the front-end systems that process large network streams; hence, the drive for this study which could potentially yield a highly reconfigurable and rapidly scalable solution.

Naegle, John H.; Monk, Stephen T.; Schutt, James A.; Doerfler, Douglas W.; Rajan, Mahesh R.

This presentation discusses the following topics: (1) Red Sky Background; (2) 3D Torus Interconnect Concepts; (3) Difficulties of Torus in IB; (4) New Routing Code for IB a 3D Torus; (5) Red Sky 3D Torus Implementation; and (6) Managing a Large IB Machine. Computing at Sandia: (1) Capability Computing - Designed for scaling of single large runs, Usually proprietary for maximum performance, and Red Storm is Sandia's current capability machine; (2) Capacity Computing - Computing for the masses, 100s of jobs and 100s of users, Extreme reliability required, Flexibility for changing workload, Thunderbird will be decommissioned this quarter, Red Sky is our future capacity computing platform, and Red Mesa machine for National Renewable Energy Lab. Red Sky main themes are: (1) Cheaper - 5X capacity of Tbird at 2/3 the cost, Substantially cheaper per flop than our last large capacity machine purchase; (2) Leaner - Lower operational costs, Three security environments via modular fabric, Expandable, upgradeable, extensible, and Designed for 6yr. life cycle; and (3) Greener - 15% less power-1/6th power per flop, 40% less water-5M gallons saved annually, 10X better cooling efficiency, and 4x denser footprint.