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

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

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

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

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

Relations between electron contentand peak density: regular and extreme behaviour

Electron content statistics offers important information for planning and operation of various application systems that make use of the transionospheric propagation of radio signals. Electron content statistics meet with an important difficulty: the majority of data stem from observations on the radio signals of orbiting satellites. The database for vertical electron content derived from observations of radio signals emitted from geostationary satellites is only very small. Therefore it is important to make use of statistics for peak density from ionosonde measurements. Quantile statistics do not contain information about ionization extremes. Therefore it is necessary to complement the statistics with case studies which reveal unusual and extreme electron content structures in space and in time