Publications

Final report for Cognitive Computing for Security LDRD 165613. It reports on the development of hybrid of general purpose/ne uromorphic computer architecture, with an emphasis on potential implementation with memristors.

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The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressing four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.

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We study a time-parallel approach to solving quadratic optimization problems with linear time-dependent partial differential equation (PDE) constraints. These problems arise in formulations of optimal control, optimal design and inverse problems that are governed by parabolic PDE models. They may also arise as subproblems in algorithms for the solution of optimization problems with nonlinear time-dependent PDE constraints, e.g., in sequential quadratic programming methods. We apply a piecewise linear finite element discretization in space to the PDE constraint, followed by the Crank-Nicolson discretization in time. The objective function is discretized using finite elements in space and the trapezoidal rule in time. At this point in the discretization, auxiliary state variables are introduced at each discrete time interval, with the goal to enable: (i) a decoupling in time; and (ii) a fixed-point iteration to recover the solution of the discrete optimality system. The fixed-point iterative schemes can be used either as preconditioners for Krylov subspace methods or as smoothers for multigrid (in time) schemes. We present promising numerical results for both use cases.

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In this report we formulate eigenvalue-based methods for model calibration using a PDE-constrained optimization framework. We derive the abstract optimization operators from first principles and implement these methods using Sierra-SD and the Rapid Optimization Library (ROL). To demon- strate this approach, we use experimental measurements and an inverse solution to compute the joint and elastic foam properties of a low-fidelity unit (LFU) model.

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This paper presents an end-to-end design process for compliance minimization based topological optimization of cellular structures through to the realization of a final printed product. Homogenization is used to derive properties representative of these structures through direct numerical simulation of unit cell models of the underlying periodic structure. The resulting homogenized properties are then used assuming uniform distribution of the cellular structure to compute the final macro-scale structure. A new method is then presented for generating an STL representation of the final optimized part that is suitable for printing on typical industrial machines. Quite fine cellular structures are shown to be possible using this method as compared to other approaches that use nurb based CAD representations of the geometry. Finally, results are presented that illustrate the fine-scale stresses developed in the final macro-scale optimized part and suggestions are made as to incorporate these features into the overall optimization process.

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As transistors start to approach fundamental limits and Moore's law slows down, new devices and architectures are needed to enable continued performance gains. New approaches based on RRAM (resistive random access memory) or memristor crossbars can enable the processing of large amounts of data[1, 2]. One of the most promising applications for RRAM crossbars is brain inspired or neuromorphic computing[3, 4].

As transistors start to approach fundamental limits and Moore's law slows down, new devices and architectures are needed to enable continued performance gains. New approaches based on RRAM (resistive random access memory) or memristor crossbars can enable the processing of large amounts of data[1, 2]. One of the most promising applications for RRAM crossbars is brain inspired or neuromorphic computing[3, 4].

Millivolt switches will not only improve energy efficiency, but will enable a new capability to manage the energy-reliability tradeoff. By effectively utilizing this system-level capability, it may be possible to obtain one or two additional generations of scaling beyond current projections. Millivolt switches will enable further energy scaling, a process that is expected to continue until the technology encounters thermal noise errors [Theis 10]. If thermal noise errors can be accommodated at higher levels through a new form of error correction, it may be possible to scale about 3× lower in system energy than is currently projected. A general solution to errors would also address long standing problems with Cosmic Ray strikes, weak and aging parts, some cyber security vulnerabilities, etc.

Millivolt switches will not only improve energy efficiency, but will enable a new capability to manage the energy-reliability tradeoff. By effectively utilizing this system-level capability, it may be possible to obtain one or two additional generations of scaling beyond current projections. Millivolt switches will enable further energy scaling, a process that is expected to continue until the technology encounters thermal noise errors [Theis 10]. If thermal noise errors can be accommodated at higher levels through a new form of error correction, it may be possible to scale about 3× lower in system energy than is currently projected. A general solution to errors would also address long standing problems with Cosmic Ray strikes, weak and aging parts, some cyber security vulnerabilities, etc.