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Functionality and security co-design environment for embedded systems

September 25, 2018
  |
Conference Paper
Author:
Jacob E. Leemaster
…
Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 25-27 September 2018.
Topic:
embedded computing
R&D area:
R&D group:

Summary

For decades, embedded systems, ranging from intelligence, surveillance, and reconnaissance (ISR) sensors to electronic warfare and electronic signal intelligence systems, have been an integral part of U.S. Department of Defense (DoD) mission systems. These embedded systems are increasingly the targets of deliberate and sophisticated attacks. Developers thus need to focus equally on functionality and security in both hardware and software development. For critical missions, these systems must be entrusted to perform their intended functions, prevent attacks, and even operate with resilience under attacks. The processor in a critical system must thus provide not only a root of trust, but also a foundation to monitor mission functions, detect anomalies, and perform recovery. We have developed a Lincoln Asymmetric Multicore Processing (LAMP) architecture, which mitigates adversarial cyber effects with separation and cryptography and provides a foundation to build a resilient embedded system. We will describe a design environment that we have created to enable the co-design of functionality and security for mission assurance.
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Summary

For decades, embedded systems, ranging from intelligence, surveillance, and reconnaissance (ISR) sensors to electronic warfare and electronic signal intelligence systems, have been an integral part of U.S. Department of Defense (DoD) mission systems. These embedded systems are increasingly the targets of deliberate and sophisticated attacks. Developers thus need to focus...

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Functionality and security co-design environment for embedded systems

Simulation approach to sensor placement using Unity3D

September 25, 2018
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Conference Paper
Author:
Kimberlee Chestnut Chang
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Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 25-27 September 2018.
Topic:
high performance computing
R&D area:
R&D group:

Summary

3D game simulation engines have demonstrated utility in the areas of training, scientific analysis, and knowledge solicitation. This paper will make the case for the use of 3D game simulation engines in the field of sensor placement optimization. Our study used a series of parallel simulations in the Unity3D simulation framework to answer the questions: how many sensors of various modalities are required and where they should be placed to meet a desired threat detection threshold? The result is a framework that not only answers this sensor placement question, but can be easily expanded to differing optimization criteria as well as answer how a particular configuration responds to differing crowd flows or informed/non-informed adversaries. Additionally, we demonstrate the scalability of this framework by running parallel instances on a supercomputing grid and illustrate the processing speed gained.
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Summary

3D game simulation engines have demonstrated utility in the areas of training, scientific analysis, and knowledge solicitation. This paper will make the case for the use of 3D game simulation engines in the field of sensor placement optimization. Our study used a series of parallel simulations in the Unity3D simulation...

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Simulation approach to sensor placement using Unity3D

Large-scale Bayesian kinship analysis

September 25, 2018
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Conference Paper
Author:
Siddharth S. Samsi
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Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 25-27 September 2018.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Kinship prediction in forensics is limited to first degree relatives due to the small number of short tandem repeat loci characterized. The Genetic Chain Rule for Probabilistic Kinship Estimation can leverage large panels of single nucleotide polymorphisms (SNPs) or sets of sequence linked SNPs, called haploblocks, to estimate more distant relationships between individuals. This method uses allele frequencies and Markov Chain Monte Carlo methods to determine kinship probabilities. Allele frequencies are a crucial input to this method. Since these frequencies are estimated from finite populations and many alleles are rare, a Bayesian extension to the algorithm has been developed to determine credible intervals for kinship estimates as a function of the certainty in allele frequency estimates. Generation of sufficiently large samples to accurately estimate credible intervals can take significant computational resources. In this paper, we leverage hundreds of compute cores to generate large numbers of Dirichlet random samples for Bayesian kinship prediction. We show that it is possible to generate 2,097,152 random samples on 32,768 cores at a rate of 29.68 samples per second. The ability to generate extremely large number of samples enables the computation of more statistically significant results from a Bayesian approach to kinship analysis.
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Summary

Kinship prediction in forensics is limited to first degree relatives due to the small number of short tandem repeat loci characterized. The Genetic Chain Rule for Probabilistic Kinship Estimation can leverage large panels of single nucleotide polymorphisms (SNPs) or sets of sequence linked SNPs, called haploblocks, to estimate more distant...

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Large-scale Bayesian kinship analysis

Interactive supercomputing on 40,000 cores for machine learning and data analysis

September 25, 2018
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Conference Paper
Author:
Albert I. Reuther
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Published in:
IEEE High Performance Extreme Computing Conf., HPEC, 25-27 September 2018.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Interactive massively parallel computations are critical for machine learning and data analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing Center (LLSC) and has required the LLSC to develop unique interactive supercomputing capabilities. Scaling interactive machine learning frameworks, such as TensorFlow, and data analysis environments, such as MATLAB/Octave, to tens of thousands of cores presents many technical challenges – in particular, rapidly dispatching many tasks through a scheduler, such as Slurm, and starting many instances of applications with thousands of dependencies. Careful tuning of launches and prepositioning of applications overcome these challenges and allow the launching of thousands of tasks in seconds on a 40,000-core supercomputer. Specifically, this work demonstrates launching 32,000 TensorFlow processes in 4 seconds and launching 262,000 Octave processes in 40 seconds. These capabilities allow researchers to rapidly explore novel machine learning architecture and data analysis algorithms.
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Summary

Interactive massively parallel computations are critical for machine learning and data analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing Center (LLSC) and has required the LLSC to develop unique interactive supercomputing capabilities. Scaling interactive machine learning frameworks, such as TensorFlow, and data analysis environments, such as...

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Interactive supercomputing on 40,000 cores for machine learning and data analysis

Linear and rotational microhydraulic actuators driven by electrowetting

September 19, 2018
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Journal Article
Author:
Jakub T. Kedzierski
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Published in:
Sci. Robot., Vol. 3, No. 22, 19 September 2018.
Topic:
microfluidics
R&D area:
R&D group:

Summary

Microhydraulic actuators offer a new way to convert electrical power to mechanical power on a microscale with an unmatched combination of power density and efficiency. Actuators work by combining surface tension force contributions from a large number of droplets distorted by electrowetting electrodes. This paper reports on the behavior of microgram-scale linear and rotational microhydraulic actuators with output force/weight ratios of 5500, cycle frequencies of 4 kilohertz, <1-micrometer movement precision, and accelerations of 3 kilometers/second. The power density and the efficiency of the actuators were characterized by simultaneously measuring the mechanical work performed and the electrical power applied. Maximum output power density was 0.93 kilowatt/kilogram, comparable with the best electric motors. At maximum power, the actuator was 60% efficient, but efficiencies were as high as 83% at lower power. Rotational actuators demonstrated a torque density of 79 newton meters/kilogram, substantially more than electric motors of comparable diameter. Scaling the droplet pitch from 100 to 48 micrometers increased power density from 0.27 to 0.93 kilowatt/kilogram, validating the quadratic scaling of actuator power.
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Summary

Microhydraulic actuators offer a new way to convert electrical power to mechanical power on a microscale with an unmatched combination of power density and efficiency. Actuators work by combining surface tension force contributions from a large number of droplets distorted by electrowetting electrodes. This paper reports on the behavior of...

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Linear and rotational microhydraulic actuators driven by electrowetting

Neural network topologies for sparse training

September 14, 2018
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Conference Paper
Author:
Ryan Allen Robinett
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Published in:
https://arxiv.org/abs/1809.05242
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known as a sparse neural network. More recent work has demonstrated the remarkable result that certain sparse DNNs can train to the same precision as dense DNNs at lower runtime and storage cost. An intriguing class of these sparse DNNs is the X-Nets, which are initialized and trained upon a sparse topology with neither reference to a parent dense DNN nor subsequent pruning. We present an algorithm that deterministically generates sparse DNN topologies that, as a whole, are much more diverse than X-Net topologies, while preserving X-Nets' desired characteristics.
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Summary

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity of hardware to store and train them. Research over the past few decades has explored the prospect of sparsifying DNNs before, during, and after training by pruning edges from the underlying topology. The resulting neural network is known...

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Neural network topologies for sparse training

Don't even ask: database access control through query control

September 1, 2018
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Journal Article
Author:
Richard Shay
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Published in:
SIGMOD Record, Vol. 47, No. 3, September 2018, pp. 17-22.
Topic:
cyber security
R&D area:
R&D group:

Summary

This paper presents a vision and description for query control, which is a paradigm for database access control. In this model, individual queries are examined before being executed and are either allowed or denied by a pre-defined policy. Traditional view-based database access control requires the enforcer to view the query, the records, or both. That may present difficulty when the enforcer is not allowed to view database contents or the query itself. This discussion of query control arises from our experience with privacy-preserving encrypted databases, in which no single entity learns both the query and the database contents. Query control is also a good fit for enforcing rules and regulations that are not well-addressed by view-based access control. With the rise of federated database management systems, we believe that new approaches to access control will be increasingly important.
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Summary

This paper presents a vision and description for query control, which is a paradigm for database access control. In this model, individual queries are examined before being executed and are either allowed or denied by a pre-defined policy. Traditional view-based database access control requires the enforcer to view the query...

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Don't even ask: database access control through query control

Human-machine collaborative optimization via apprenticeship scheduling

September 1, 2018
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Journal Article
Author:
Matthew C. Gombolay
…
Published in:
J. Artif. Intell. Res., Vol. 63, 2018, pp. 1-49.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the "single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes. We propose a new approach for capturing this decision-making process through counterfactual reasoning in pairwise comparisons. Our approach is model-free and does not require iterating through the state space. We demonstrate that this approach accurately learns multifaceted heuristics on a synthetic and real world data sets. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of schedule optimization. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates optimal solutions up to 9.5 times faster than a state-of-the-art optimization algorithm.
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Summary

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale...

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Human-machine collaborative optimization via apprenticeship scheduling

Valleytronics: opportunities, challenges, and paths forward

August 13, 2018
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Journal Article
Author:
Steven A. Vitale
…
Published in:
Small, 13 August 2018, 1801483.
Topic:
computing
R&D area:
R&D group:

Summary

A lack of inversion symmetry coupled with the presence of time-reversal symmetry endows 2D transition metal dichalcogenides with individually addressable valleys in momentum space at the K and K' points in the first Brillouin zone. This valley addressability opens up the possibility of using the momentum state of electrons, holes, or excitons as a completely new paradigm in information processing. The opportunities and challenges associated with manipulation of the valley degree of freedom for practical quantum and classical information processing applications were analyzed during the 2017 Workshop on Valleytronic Materials, Architectures, and Devices; this Review presents the major findings of the workshop.
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Summary

A lack of inversion symmetry coupled with the presence of time-reversal symmetry endows 2D transition metal dichalcogenides with individually addressable valleys in momentum space at the K and K' points in the first Brillouin zone. This valley addressability opens up the possibility of using the momentum state of electrons, holes...

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Valleytronics: opportunities, challenges, and paths forward

Fuel production systems for remote areas via an aluminum energy vector

July 29, 2018
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Journal Article
Author:
Eric R. Morgan
Published in:
Energy Fuels, Vol. 32, no. 9, 2018, pp. 9033-9042.
Topic:
energy systems
R&D area:
R&D group:

Summary

Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid diesel fuels. On a volumetric basis, aluminum has more than twice the energy of diesel fuel, making it a superb structural energy vector for remote applications. Once aluminum is treated with gallium, water of nearly any purity is used to rapidly oxidize the aluminum metal which spontaneously evolves hydrogen and heat in roughly equal energetic quantities. The benign byproduct of the reaction could, in theory, be taken to an off-site facility and recycled back into aluminum using standard smelting processes or it could be left onsite as a high-value waste. The hydrogen can easily be used as a feedstock for diesel fuel, via Fischer-Tropsch (FT) reaction mechanisms, while the heat can be leveraged for other processes, including synthesis gas compression. It is shown that as long as a carbon source, such as diesel fuel, is already present, additional diesel can be made by recovering and recycling the CO2 in the diesel exhaust. The amount of new diesel that can be made is directly related to the fraction of available CO2 that is recovered, with 100% recovery being equivalent to doubling the diesel fuel. The volume of aluminum required to accomplish this is lower than simply bringing twice as much diesel and results in a 50% increase in volumetric energy density. That is, 50% fewer fuel convoys would be required for fuel delivery. Moreover, aluminum has the potential to be exploited as a structural fuel that can be used as pallets, containers, etc., before being consumed to produce diesel. Furthermore, FT diesel production via aluminum and CO2 can be achieved without sacrificing electrical power generation.
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Summary

Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid...

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Fuel production systems for remote areas via an aluminum energy vector
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