architect: Arbitrary-precision Constant-hardware Iterative Compute

Many algorithms feature an iterative loop that converges to the result of interest. The numerical operations in such algorithms are generally implemented using finite-precision arithmetic, either fixed or floating point, most of which operate least-significant digit first. This results in a fundamental problem: if, after some time, the result has not converged, is this because we have not run the algorithm for enough iterations or because the arithmetic in some iterations was insufficiently precise? There is no easy way to answer this question, so users will often over-budget precision in the hope that the answer will always be to run for a few more iterations. We propose a fundamentally new approach: armed with the appropriate arithmetic able to generate results from most-significant digit first, we show that fixed compute-area hardware can be used to calculate an arbitrary number of algorithmic iterations to arbitrary precision, with both precision and iteration index increasing in lockstep. Thus, datapaths constructed following our principles demonstrate efficiency over their traditional arithmetic equivalents where the latter’s precisions are either under- or over-budgeted for the computation of a result to a particular accuracy. For the execution of 100 iterations of the Jacobi method, we obtain a 1.60x increase in frequency and 15.7x LUT and 50.2x flip-flop reductions over a 2048-bit parallel-in, serial-out traditional arithmetic equivalent, along with 46.2x LUT and 83.3x flip-flop decreases versus the state-of-the-art online arithmetic implementation

Nonideality‐Aware Training for Accurate and Robust Low‐Power Memristive Neural Networks

Recent years have seen a rapid rise of artificial neural networks being employed in a number of cognitive tasks. The ever-increasing computing requirements of these structures have contributed to a desire for novel technologies and paradigms, including memristor-based hardware accelerators. Solutions based on memristive crossbars and analog data processing promise to improve the overall energy efficiency. However, memristor nonidealities can lead to the degradation of neural network accuracy, while the attempts to mitigate these negative effects often introduce design trade-offs, such as those between power and reliability. In this work, we design nonideality-aware training of memristor-based neural networks capable of dealing with the most common device nonidealities. We demonstrate the feasibility of using high-resistance devices that exhibit high $I$-$V$ nonlinearity -- by analyzing experimental data and employing nonideality-aware training, we estimate that the energy efficiency of memristive vector-matrix multipliers is improved by three orders of magnitude ($0.715\ \mathrm{TOPs}^{-1}\mathrm{W}^{-1}$ to $381\ \mathrm{TOPs}^{-1}\mathrm{W}^{-1}$) while maintaining similar accuracy. We show that associating the parameters of neural networks with individual memristors allows to bias these devices towards less conductive states through regularization of the corresponding optimization problem, while modifying the validation procedure leads to more reliable estimates of performance. We demonstrate the universality and robustness of our approach when dealing with a wide range of nonidealities

Quantitative study of hydration of C3S and C2S by thermal analysis. Evolution and composition of C-S-H gels formed

This research is part of a European project (namely, CODICE project), main objective of which is modelling, at a multi-scale, the evolution of the mechanical performance of non-degraded and degraded cementitious matrices. For that, a series of experiments were planned with pure synthetic tri-calcium silicate (C3S) and bi-calcium silicate (C2S) (main components of the Portland cement clinker) to obtain different calcium–silicate–hydrate (C–S–H) gel structures during their hydration. The characterization of those C–S–H gels and matrices will provide experimental parameters for the validation of the multi-scale modelling scheme proposed. In this article, a quantitative method, based on thermal analyses, has been used for the determination of the chemical composition of the C–S–H gel together with the degree of hydration and quantitative evolution of all the components of the pastes. Besides, the microstructure and type of silicate tetrahedron and mean chain length (MCL) were studied by scanning electron microscopy (SEM) and 29Si magic-angle-spinning (MAS) NMR, respectively. The main results showed that the chemical compositions for the C–S–H gels have a CaO/SiO2 M ratio almost constant of 1.7 for both C3S and C2S compounds. Small differences were found in the gel water content: the H2O/SiO2 M ratio ranged from 2.9 ± 0.2 to 2.6 ± 0.2 for the C3S (decrease) and from 2.4 ± 0.2 to 3.2 ± 0.2 for the C2S (increase). The MCL values of the C–S–H gels, determined from 29Si MAS NMR, were 3.5 and 4 silicate tetrahedron, for the hydrated C3S and C2S, respectively, remaining almost constant at all hydration periods

On the symmetries of BF models and their relation with gravity

The perturbative finiteness of various topological models (e.g. BF models) has its origin in an extra symmetry of the gauge-fixed action, the so-called vector supersymmetry. Since an invariance of this type also exists for gravity and since gravity is closely related to certain BF models, vector supersymmetry should also be useful for tackling various aspects of quantum gravity. With this motivation and goal in mind, we first extend vector supersymmetry of BF models to generic manifolds by incorporating it into the BRST symmetry within the Batalin-Vilkovisky framework. Thereafter, we address the relationship between gravity and BF models, in particular for three-dimensional space-time.Comment: 29 page

OMAE2006-92674 ANALYSIS OF TURBULENT FLOWS AND VIV OF TRUSS SPAR RISERS

ABSTRACT Complex flows through riser arrays, such as the case of risers located in the truss section of a truss spar, are very difficult to describe and analyze. It is especially difficult predict and correct Vortex Induced Vibration (VIV) response using traditional tools that were meant to analyze single risers rather than arrays of risers. Computational Fluid Dynamics (CFD) offers the designer the capability to properly analyze these complex problems, increasing the reliability of the design. In this study, a full scale truss spar with vertical risers is modeled using CFD. The VIV response of the risers is predicted and the effect of risers is correctly captured and compared with experiments

Hardware compilation of deep neural networks: an overview

Deploying a deep neural network model on a reconfigurable platform, such as an FPGA, is challenging due to the enormous design spaces of both network models and hardware design. A neural network model has various layer types, connection patterns and data representations, and the corresponding implementation can be customised with different architectural and modular parameters. Rather than manually exploring this design space, it is more effective to automate optimisation throughout an end-to-end compilation process. This paper provides an overview of recent literature proposing novel approaches to achieve this aim. We organise materials to mirror a typical compilation flow: front end, platform-independent optimisation and back end. Design templates for neural network accelerators are studied with a specific focus on their derivation methodologies. We also review previous work on network compilation and optimisation for other hardware platforms to gain inspiration regarding FPGA implementation. Finally, we propose some future directions for related research

Social media use and impact during the holiday travel planning process

Through an empirical study among holiday travellers, residing in the Former Soviet Union Republics, this paper presents a comprehensive view of role and impact of social media on the whole holiday travel planning process: Before, during and after the trip, providing insights on usage levels, scope of use, level of influence and trust. Findings suggest that social media are predominantly used after holidays for experience sharing. It is also shown that there is a strong correlation between perceived level of influence from social media and changes made in holiday plans prior to final decisions. Moreover, it is revealed that user-generated content is perceived as more trustworthy when compared to official tourism websites, travel agents and mass media advertising

Using Model Test Data to Assess VIV Factor of Safety for SCR and TTR in GOM

This paper presents results obtained as part of the DeepStar Phase 10 program on VIV Factors of Safety. The objective was to develop a general methodology to calibrate Factors of Safety for VIV-induced fatigue and to apply it to partially straked risers. This was achieved using reliability methods, accepted industry VIV prediction software and state-of-the-art model test experiments. Most oil companies use a Factor of Safety of 20 when predicting VIV damage using VIV software tools. There are numerous software tools currently in use in industry to predict VIV damage to straked risers and each of them will have different accuracy, and therefore an intrinsic level of conservatism. Understanding the level of conservatism in different VIV prediction software is therefore critical to determining what Factor of Safety to use. This study benchmarks the latest generation of industry accepted VIV design tools at the time of the study (2011): SHEAR7v4.6, VIVAv6.5 and VIVANAv3.7.24 against high quality VIV data from three separate straked riser experiments. A bias distribution (predicted to measured VIV damage results) is obtained for each software tool as a function of the strake coverage. A novel reliability framework approach is then developed to incorporate all uncertainties associated with VIV fatigue prediction into a limit state function, including variability in met-ocean conditions and variability in the fatigue resistance of the material characterized by a design S-N curve. The limit state function is analyzed using First Order Reliability Methods to develop Factors of Safety for target probabilities of failure. The general method is then applied on two case studies involving an SCR and TTR in Gulf of Mexico loop currents, but it can be easily extended to different locations and riser configurations. The resulting FoS range from about 1 to 15 for most software, and are lower than industry standards for VIV prediction. The FoS do not vary markedly for different riser configurations, indicating the possibility of reducing excess conservatism when predicting VIV damage on straked risers.DeepStar (Consortium)SHEAR7 JI

Simulating temporal evolution of pressure in two-phase flow in porous media

We have simulated the temporal evolution of pressure due to capillary and viscous forces in two-phase drainage in porous media. We analyze our result in light of macroscopic flow equations for two-phase flow. We also investigate the effect of the trapped clusters on the pressure evolution and on the effective permeability of the system. We find that the capillary forces play an important role during the displacements for both fast and slow injection rates and both when the invading fluid is more or less viscous than the defending fluid. The simulations are based on a network simulator modeling two-phase drainage displacements on a two-dimensional lattice of tubes.Comment: 12 pages, LaTeX, 14 figures, Postscrip