285 research outputs found
Towards efficient on-board deployment of DNNs on intelligent autonomous systems
With their unprecedented performance in major AI tasks, deep neural networks (DNNs) have emerged as a primary building block in modern autonomous systems. Intelligent systems such as drones, mobile robots and driverless cars largely base their perception, planning and application-specific tasks on DNN models. Nevertheless, due to the nature of these applications, such systems require on-board local processing in order to retain their autonomy and meet latency and throughput constraints. In this respect, the large computational and memory demands of DNN workloads pose a significant barrier on their deployment on the resource-and power-constrained compute platforms that are available on-board. This paper presents an overview of recent methods and hardware architectures that address the system-level challenges of modern DNN-enabled autonomous systems at both the algorithmic and hardware design level. Spanning from latency-driven approximate computing techniques to high-throughput mixed-precision cascaded classifiers, the presented set of works paves the way for the on-board deployment of sophisticated DNN models on robots and autonomous systems
A throughput-latency co-optimised cascade of convolutional neural network classifiers
Convolutional Neural Networks constitute a promi-nent AI model for classification tasks, serving a broad span ofdiverse application domains. To enable their efficient deploymentin real-world tasks, the inherent redundancy of CNNs is fre-quently exploited to eliminate unnecessary computational costs.Driven by the fact that not all inputs require the same amount ofcomputation to drive a confident prediction, multi-precision cas-cade classifiers have been recently introduced. FPGAs comprise apromising platform for the deployment of such input-dependentcomputation models, due to their enhanced customisation ca-pabilities. Current literature, however, is limited to throughput-optimised cascade implementations, employing large batching atthe expense of a substantial latency aggravation prohibiting theirdeployment on real-time scenarios. In this work, we introduce anovel methodology for throughput-latency co-optimised cascadedCNN classification, deployed on a custom FPGA architecturetailored to the target application and deployment platform,with respect to a set of user-specified requirements on accuracyand performance. Our experiments indicate that the proposedapproach achieves comparable throughput gains with relatedstate-of-the-art works, under substantially reduced overhead inlatency, enabling its deployment on latency-sensitive applications
Time-dependent prediction degradation assessment of neural-networks-based TEC forecasting models
An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of +/-10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the f(0)F2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.info:eu-repo/semantics/publishedVersio
The Weyl tensor two-point function in de Sitter spacetime
We present an expression for the Weyl-Weyl two-point function in de Sitter
spacetime, based on a recently calculated covariant graviton two-point function
with one gauge parameter. We find that the Weyl-Weyl two-point function falls
off with distance like r^{-4}, where r is spacelike coordinate separation
between the two points.Comment: 9 pages, no figure
Large-distance behaviour of the graviton two-point function in de Sitter spacetime
It is known that the graviton two-point function for the de Sitter invariant
"Euclidean" vacuum in a physical gauge grows logarithmically with distance in
spatially-flat de Sitter spacetime. We show that this logarithmic behaviour is
a gauge artifact by explicitly demonstrating that the same behaviour can be
reproduced by a pure-gauge two-point function.Comment: 19 pages, no figures, misprints and minor errors correcte
Dynamic Modification and Damage Propagation of a Two-Storey Full-Scale Masonry Building
The progressive change of modal characteristics due to accumulated damage on an unreinforced masonry (URM) building is investigated. The stone URM building, submitted to five consecutive shakings, has been experimentally studied on the shaking table of EUCENTRE laboratory (Pavia, Italy). The dynamic characteristics of the test specimen are analytically estimated using frequency and state-space modal identification from ambient vibration stationary tests carried out before the strong motion transient tests at various levels of damage. A singular value (SV) decomposition of the cross-correlation matrix of the acceleration response in the frequency domain is applied to determine the modal characteristics. In the time domain, the subspace state-space system identification is performed. Modal characteristics evolve from the initial linear state up to the ultimate collapse state in correlation with accumulated damage. Modal frequencies shorten with increasing intensity, whereas modal damping ratios are enhanced. Modal shapes also change with increasing level of accumulated damage. Comparing the evolution of modal characteristics, it is concluded that modal damping ratio shift can be better correlated with the system's actual performance giving a better representation of damage than that of natural frequency shift ratio or the modes difference
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A practice-oriented model for pushover analysis of a class of timber-framed masonry buildings
Timber-Framed (TF) masonry is a structural system characterized by high complexity and diversity. Limited experimental and analytical research has been carried out so far to explore their earthquake response, partly due to the complexity of the problem and partly due to the scarcity of TF buildings across the world. Here, a new practice-oriented non-linear (NL) macro-model is presented for TF masonry structures, based on the familiar diagonal strut approach with NL axial hinges in the struts. The constitutive law for the hinges (axial force vs. axial deformation) is derived on the basis of an extensive parametric analysis of the main factors affecting the response of TF masonry panels subjected to horizontal loading. The parameters studied are related to the geometric features of the panel and the strength of wood as well as the connections of the timber elements. The parametric analysis is performed using a micro-model based on Hill-type plasticity and it is shown that in the studied X-braced walls the masonry infills do not make a significant contribution to the lateral load resistance. Empirical expressions are proposed for the yield and maximum displacement and shear of a horizontally loaded TF panel. The model is verified against available experimental data, and is found to capture well the envelopes of the experimental loops. The model is readily applicable to NL static analysis (pushover) analysis for the assessment of the lateral load capacity of TF masonry buildings, as the number of input parameters for deriving the constitutive law has been limited to only five
TEC and foF2 variations: preliminary results
Investigation of the relationship between TEC and (foF2)2 shows that although they are highly correlated, a «hysteresis» effect exists between them. The slab thickness is greater before than after mid-day for equal cos ?values. Moreover, a comparison of the calculated upper and lower quartiles of variability in TEC, foF2 and Nmax, respectively shows that the variability of TEC lies between those of foF2 and Nmax depending on the level of solar activity
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