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A Probabilistic Future for Neuromorphic Computing
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A Probabilistic Future for Neuromorphic Computing
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The future impact of computation on planetary exploration
Informed speculations are advanced on plausible candidates for space exploration missions in decades ahead; while none are approved NASA projects, they are deemed accessible to current technology. A Venus radar mapper, a minirover system (several rovers) for Mars surface exploration (geology, areochemistry, meteorology, detection of biota), investigation of Halley's comet (1986 apparition) with the aid of a solar sail vehicle, flybys of Jovian (Galilean) satellites and a landing on Ganymede, and a soft landing on the Saturnian large satellite Titan for examination of its atmosphere and surface are sketched. Anticipated data rates, software, mission reliability, and spacecraft autonomy are discussed, along with anticipated improvements in information transmission hardware and software, and some conjectures beyond the turn of the century.
A view toward future fluid dynamics computing
Advances in computational fluid dynamics are paced by simulation methodology and computer resources. Examples of three-dimensional fluid dynamic simulations are presented to illustrate recent developments in equation modeling and numerical methods and to point out the need for increased computer power. Electronic technology dictates that to fill this need, computers will be based on parallel processing principles. The identification of parallelism in three dimensions is illustrated by examining an implicit, approximate-factorization approach to the Navier-Stokes equations. Finally, two computer concepts aimed at satisfying the demands of the three-dimensional Reynolds averaged Navier-Stokes simulations are discussed.
Evaluation of existing and proposed computer architectures for future ground-based systems
Parallel processing architectures and techniques used in current supercomputers are described and projections are made of future advances. Presently, the von Neumann sequential processing pattern has been accelerated by having separate I/O processors, interleaved memories, wide memories, independent functional units and pipelining. Recent supercomputers have featured single-input, multiple data stream architectures, which have different processors for performing various operations (vector or pipeline processors). Multiple input, multiple data stream machines have also been developed. Data flow techniques, wherein program instructions are activated only when data are available, are expected to play a large role in future supercomputers, along with increased parallel processor arrays. The enhanced operational speeds are essential for adequately treating data from future spacecraft remote sensing instruments such as the Thematic Mapper.
Pilots of the future - Human or computer?
In connection with the occurrence of aircraft accidents and the evolution of the air-travel system, questions arise regarding the computer's potential for making fundamental contributions to improving the safety and reliability of air travel. An important result of an analysis of the causes of aircraft accidents is the conclusion that humans - 'pilots and other personnel' - are implicated in well over half of the accidents which occur. Over 70 percent of the incident reports contain evidence of human error. In addition, almost 75 percent show evidence of an 'information-transfer' problem. Thus, the question arises whether improvements in air safety could be achieved by removing humans from control situations. In an attempt to answer this question, it is important to take into account also certain advantages which humans have in comparison to computers. Attention is given to human error and the effects of technology, the motivation to automate, aircraft automation at the crossroads, the evolution of cockpit automation, and pilot factors.
Biological neural networks as model systems for designing future parallel processing computers
One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.
Future directions in computing and CFD
In recent years CFD on massively parallel machines has become a reality. This paper summarizes some recent trends both in high performance computing, and in CFD using parallel machines. The long term computational requirements for accomplishing some of the large scale problems in computational aerosciences, and current hardware and architecture trends are discussed. Performance results obtained from the implementation of some CFD applications on the Connection Machine CM-2 and the Intel iPSC/860 at NASA Ames Research Center are presented. It is argued that only massively parallel machines will be able to meet these grand challenge requirements.
A Future View of Computational Science in Aircraft Engine Design
The accomplishments of LeRC in the field of computational fluid dynamics are presented.
Computational Needs for Future Direct Imaging Exoplanet Mission Concepts
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Computational Math Problems for a Clean Energy Future
Cutting edge computational mathematics are ubiquitous in renewable energy research. Problems in resilient and reliable electric grid operations, infrastructure planning, wind farm yaw control, and more demand sophisticated and scalable computational tools that enable the transition of renewable energy technologies from proof of concept to deployment into our energy system. The mission of the Computational Science Center at NREL is to lead the lab's efforts to solve energy challenges using high-performance computing (HPC), computational science, applied mathematics, scientific data management, visualization, and informatics. In this poster, we provide a short overview of three areas of computational mathematics research at NREL: wind power scenario generation for stochastic grid operations and infrastructure planning, improved rational function approximations for electromagnetic transients codes, and wind farm yaw control using a combination of the Alternating Direction Method of Multipliers (ADMM) and reinforcement learning (RL). Increasing penetrations of renewable energy into power grids motivate the investigation of new approaches to characterizing uncertainty for five-minute economic dispatch problems. Similarly, as the penetration of distributed energy resources on power grids increases, it becomes important to revisit our methods of modelling transient phenomena, i.e. electromagnetic transients programs. Finally, the combination of ADMM and RL for wind farm yaw control presented here can potentially increase the efficiency of the deployed distributed controllers by orders of magnitude.
Opportunities for neuromorphic computing algorithms and applications
Neuromorphic computing technologies will be important for the future of computing, but much of the work in neuromorphic computing has focused on hardware development. In this study, we review recent results in neuromorphic computing algorithms and applications. We highlight characteristics of neuromorphic computing technologies that make them attractive for the future of computing and we discuss opportunities for future development of algorithms and applications on these systems.
Water Quality for High Performance Computing
To support future generations of high performance computers, the Advanced Simulation and Computing Program has a mission need for 80MW of peak power and cooling. Currently the water for the cooling towers at the Strategic Computing Complex (SCC) are cooled by the LANL waste water treated Sanitary Effluent Recovery Facility (SERF) or potable water from the county. The SCC cooling towers currently operate by cycling water between 2-3 cycles of concentration (COC), which is less than the ideal 6 cycles for maximizing water usage. SERF is the ideal water supplier, however when it’s not available potable water is used. The amount of water that the SCC needs nominally is around 45 Million-Gallons/Year (MGY). The future super computer infrastructure (FSI) will need around 125 MGY nominally. SERF is approaching its production limit for reclaimed water. In order to avoid using 100% potable water for the FSI, the option of using non-potable reuse water from Los Alamos County (LAC) was analyzed. This project scope is to focus on the water quality needed for the new super computer cooling towers. Once water quality needs are established, the ideal treatment process needs to be determined. Due to high silica content in water at Los Alamos, emphasis has been placed on treatment processes to remove silica. SERF’s current and future ability to treat water was assessed, as well as the cost of running a SERF like facility or alternative.
Computational aeroacoustics - Present status and future promise
The field of computational aeroacoustics (CAA), in which sound generation by unsteady flows is calculated from first principles, has recently emerged as a viable discipline. This paper describes one CAA technique which has been validated for low Mach number, low Reynolds number, two-dimensional flows. Results of its application to geometries producing both dipole and quadrupole type sound sources are presented along with theoretical and experimental comparisons. In addition, the future promise of this and other CAA techniques in removing the present limitations is evaluated.