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Real-time dense SLAM techniques aim to reconstruct the dense three-dimensional geometry of a scene in real time with an RGB or RGB-D sensor. An indoor scene is an important type of working environment for these techniques. The planar prior can be used in this scenario to improve the reconstruction quality, especially for large low-texture regions that commonly occur in an indoor scene. This article fully explores the planar prior in a dense SLAM pipeline. First, we propose a novel plane detection and segmentation method that runs at 200 Hz on a modern graphics processing unit. Our algorithm for constructing global plane constraints is very efficient; hence, we use it in the process of each input frame for the camera pose estimation while maintaining the real-time performance. Second, we propose herein a plane-based map representation that greatly reduces the memory footprint of plane regions while keeping the geometric details on planes. The experiments reveal that our system yields superior reconstruction results with planar information running at more than 30 fps. Aside from speed and storage improvements, our technique also handles the low-texture problem in plane regions.

When deploying memory-intensive applications to public clouds, one important yet challenging question to answer is how to select a specific instance type whose memory capacity is large enough to prevent out-of-memory errors while the cost is minimized without violating performance requirements. The state-of-the-practice solution is trial and error, causing both performance overhead and additional monetary cost. This paper investigates two memory scaling mechanisms in public clouds: physical memory (good performance and high cost) and virtual memory (degraded performance and no additional cost). In order to analyze the trade-off between performance and cost of the two scaling options, a performance-cost model is developed that is driven by a lightweight analytic prediction approach through a compact representation of the memory footprint. In addition, for those scenarios when the footprint is unavailable, a meta-model-based prediction method is proposed using just-in-time migration mechanisms. The proposed techniques have been extensively evaluated with various benchmarks and real-world applications on Amazon Web Services: the performance-cost model is highly accurate and the proposed just-in-time migration approach reduces the monetary cost by up to 66\%.

Due to the outbreak of recent network attacks, it is necessary to develop a robust network intrusion detection system (NIDS) that can quickly and effectively identify the network attack. Although the state-of-the-art detection algorithms have shown quite promising detection performance, they suffer from computationally intensive operations and large memory footprint, making themselves infeasible to applications at the resourceconstrained edge devices. We propose a lightweight yet effective NIDS scheme that incorporates a stacked autoencoder with a network pruning technique. By removing a set of ineffective neurons across layers in the autoencoder network with a certain probability based on their importance, a considerably large portion of relatively nominal training parameters are reduced. Then, the pruned and pre-trained encoder network is used as-is and is connected with a separate classifier network for attack type inference, avoiding a full retraining from scratch. Experimental results indicate that our stacked autoencoder-based classification network with probabilistic feature extraction has outperformed the state-of-the-art NIDSs in terms of attack detection rate. Further, we have shown that our lightweight NIDS scheme has significantly reduced the computational complexity throughout the architecture, making it feasible to the edge, while maintaining a similar attack type detection quality compared with its original fully-connected neural network.

This work presents a multi-level approximation exploration undertaken on the Ring-Learning-with-Errors (R-LWE) based Public-key Cryptographic (PKC) schemes that belong to quantum-resilient cryptography algorithms. Among the various quantum-resilient cryptography schemes proposed in the currently running NIST’s Post-quantum Cryptography (PQC) standardization plan, the lattice based LWE schemes have emerged as the most viable and preferred class for the IoT applications due to their compact area and memory footprint compared to other alternatives. However, compared to the classical schemes used today, R-LWE is much harder a challenge to fit on embedded IoT (end-node) devices, due to their stricter resource constraints (lower area, memory, energy budgets) as well as their limited computational capabilities. To the best of our knowledge, this is the first endeavour exploring the inherent approximate nature of LWE problem to undertake a multi-level Approximate R-LWE (AxRLWE) architecture with respective security estimates opt for lightweight IoT devices. Undertaking AxRLWE on Field Programmable Gate Arrays (FPGAs), we benchmarked a 64% area reduction cost compared to earlier accurate R-LWE designs at the cost of reduced quantum-security. For the Application Specific Integrated Circuits (ASICs) with 45nm CMOS technology, AxRLWE was benchmarked to fit well within the same area-budget of lightweight ECC processor and consume a third of energy compared to special class of R-Binary LWE (R-BLWE) designs being proposed for an IoT, with better security level.

Long Short-Term Memory (LSTM) achieved great success in healthcare applications. However, its extensive computation cost and massive model size have become the major obstacles for the deployment of such a powerful algorithm in resource-limited embedded systems such as wearable devices. Quantization is a promising way to reduce the memory footprint and computational cost of neural networks. Although quantization achieved remarkable success in convolutional neural networks (CNNs), it still suffers from large accuracy loss in LSTM networks, especially in extremely low bitwidths. In this paper, we propose Fast and Compact Ternary LSTM (FaCT-LSTM), which bridges the accuracy gap between the full precision and quantized neural networks. We propose a hardware-friendly circuit to implement ternarized LSTM and eliminate computation-intensive floating-point operations. With the proposed ternarized LSTM architectures, our experiments on the ECG and EMG signals show ~0.88 to 2.04% accuracy loss in comparison to the full-precision counterparts while reducing latency and area for ~111× to 116× and ~29× to 33×, respectively. The proposed architectures also improves the memory footprint and bandwidth of the full precision signal classification, by 17×, and 31×, respectively.

Multi-access Edge Computing (MEC) is booming as a promising paradigm to push the computation and communication resources from cloud to the network edge to provide services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without time-consuming backbone. Service placement at the edge is of importance to put MEC from theory into practice. However, current state-of-the-art research does not sufficiently take the composite property of services into consideration. Besides, although Kubernetes has certain abilities to heal container failures, high availability cannot be ensured due to heterogeneity and variability of edge sites. To deal with these problems, we propose a distributed redundant placement framework SAA-RP and a GA-based Server Selection (GASS) algorithm for microservice-based applications with sequential combinatorial structure. We formulate a stochastic optimization problem with the uncertainty of microservice request considered, and then decide for each microservice, how it should be deployed and with how many instances as well as on which edge sites to place them. Benchmark policies are implemented in two scenarios, where redundancy is allowed and not, respectively. Numerical results based on a real-world dataset verify that GASS significantly outperforms all the benchmark policies.

In this article, a quantized network acceleration processor (QNAP) is proposed to efficiently accelerate CNN processing by eliminating most unessential operations based on algorithm-hardware co-optimizations. First, an effective-weight-based convolution (EWC) is proposed to distinguish a group of effective weights (EWs) to replace the other unique weights. Therefore, the input activations corresponding to the same EW can be accumulated first and then multiplied by the EW to reduce amounts of multiplication operations, which is efficiently supported by the dedicated process elements in QNAP. The experimental results show that energy efficiency is improved by 1.59x-3.20x compared with different UCNN implementations. Second, an error-compensation-based prediction (ECP) method adopts trained compensated values to replace partly unimportant partial sums to further reduce potentially redundant addition operations caused by the ReLU function. Compared with SnaPEA and Pred on AlexNet, 1.23x and 1.75x higher energy efficiencies (TOPS/W) are achieved by ECP, respectively, with marginal accuracy loss. Third, the residual pipeline mode is proposed to efficiently implement residual blocks with a 1.5x lower memory footprint, a 1.18x lower power consumption, and a 13.15% higher hardware utilization on average than existing works. Finally, the QNAP processor is fabricated in the TSMC 28-nm CMOS process with a core area of 1.9 mm². Benchmarked with AlexNet, VGGNet, GoogLeNet, and ResNet on ImageNet at 470 MHz and 0.9 V, the processor achieves 117.4 frames per second with 131.6-mW power consumption on average, which outperforms the state-of-the-art processors by 1.77x-24.20x in energy efficiency.

Kernel recursive least squares (KRLS) is a widely used online machine learning algorithm for time series predictions. In this article, we present the mixed-precision KRLS, producing equivalent prediction accuracy to double-precision KRLS with a higher training throughput and a lower memory footprint. The mixed-precision KRLS applies single-precision arithmetic to the computation components being not only numerically resilient but also computationally intensive. Our mixed-precision KRLS demonstrates the 1.32, 1.15, 1.29, 1.09, and 1.08x training throughput improvements using 24.95%, 24.74%, 24.89%, 24.48%, and 24.20% less memory footprint without losing any prediction accuracy compared to double-precision KRLS for a 3-D nonlinear regression, a Lorenz chaotic time series, a Mackey-Glass chaotic time series, a sunspot number time series, and a sea surface temperature time series, respectively.

Understanding the interconnected relationships of large-scale information networks like social, scholar and Internet of Things networks is vital for tasks like recommendation and fraud detection. The vast majority of the real-world networks are inherently heterogeneous and dynamic, containing many different types of nodes and edges and can change drastically over time. The dynamicity and heterogeneity make it extremely challenging to reason about the network structure. Unfortunately, existing approaches are inadequate in modeling real-life networks as they require extensive computational resources and do not scale well to large, dynamically evolving networks. We introduce LIME, a better approach for modeling dynamic and heterogeneous information networks. LIME is designed to extract high-quality network representation with significantly lower memory resources and computational time over the state-of-the-art. Unlike prior work that uses a vector to encode each network node, we exploit the semantic relationships among network nodes to encode multiple nodes with similar semantics in shared vectors. We evaluate LIME by applying it to three representative network-based tasks, node classification, node clustering and anomaly detection, performing on three large-scale datasets. Our extensive experiments demonstrate that LIME not only reduces the memory footprint by over 80\% and computational time over 2x when learning network representation but also delivers comparable performance for downstream processing tasks.

Compressive sensing (CS) can provide joint compression and encryption, which is promising to address the challenges of massive sensor data and data security in the Internet of Things (IoT). However, as IoT devices have constrained memory, computing power, and energy, in practice the CS-based computationally secure scheme is shown to be vulnerable to ciphertext-only attack for short signal length. Although the CS-based perfectly secure scheme has no such vulnerabilities, its practical realization is challenging. In this paper, we propose an energy concealment (EC) encryption scheme, a practical realization of the perfectly secure scheme by concealing energy, thereby removing the requirement of an additional secure channel. We propose three different methods to generate sensing matrix to improve energy efficiency using linear feedback shift registers and lagged Fibonacci sequences. Leveraging the signal’s maximum energy in the EC scheme, we design a new measure to evaluate reconstructed signal quality without the knowledge of the original signal. Furthermore, a new CS decoding algorithm is designed by incorporating the knowledge of maximum energy at the decoder, which improves the signal reconstruction quality while reducing the number of measurements. Additionally, our comprehensive security analysis shows that the EC scheme is secure against various cryptographic attacks. We implement the EC scheme using the three different ways of generating the sensing matrix in the resource-constrained TelosB mote using the Contiki operating system. The experimental results demonstrate that the EC scheme outperforms AES in terms of code memory footprint and total energy consumption.

This contribution presents a method for modeling and controlling a heavy-duty biturbocharged diesel engine. The modeling scheme can incorporate expert knowledge of the control relevant combustion quantities into Gaussian process models. A nonlinear model predictive controller (MPC) is used to control the engine outputs subject to the gas path dynamics and nonlinear constraints for the emissions and for the sake of engine protection. In addition, an online learning scheme based on Gaussian process regression is used to compensate for model uncertainties due to aging effects and manufacturing tolerances. A consistent model smoothing strategy is derived to preserve the given expert knowledge and to avoid abrupt reactions of the MPC due to the online learning of the models. All parts of the controller are implemented with respect to real-time feasibility and small memory footprint. Experimental results for a real-world heavy-duty engine demonstrate the performance and the online learning ability of the presented nonlinear MPC scheme that may be transferred to various diesel engine applications.

CNNs outperform traditional machine learning algorithms across a wide range of applications. However, their computational complexity makes it necessary to design efficient hardware accelerators. Most CNN accelerators focus on exploring dataflow styles that exploit computational parallelism. However, potential performance speedup from sparsity has not been adequately addressed. The computation and memory footprint of CNNs can be significantly reduced if sparsity is exploited in network evaluations. To take advantage of sparsity, some accelerator designs explore sparsity encoding and evaluation on CNN accelerators. However, sparsity encoding is just performed on activation or weight and only in inference. It has been shown that activation and weight also have high sparsity levels during training. Hence, sparsity-aware computation should also be considered in training. To further improve performance and energy efficiency, some accelerators evaluate CNNs with limited precision. However, this is limited to the inference since reduced precision sacrifices network accuracy if used in training. In addition, CNN evaluation is usually memory-intensive, especially in training. In this paper, we propose SPRING, a SParsity-aware Reduced-precision Monolithic 3D CNN accelerator for trainING and inference. SPRING supports both CNN training and inference. It uses a binary mask scheme to encode sparsities in activation and weight. It uses the stochastic rounding algorithm to train CNNs with reduced precision without accuracy loss. To alleviate the memory bottleneck in CNN evaluation, especially in training, SPRING uses an efficient monolithic 3D NVM interface to increase memory bandwidth. Compared to GTX 1080 Ti, SPRING achieves 15.6X, 4.2X and 66.0X improvements in performance, power reduction, and energy efficiency, respectively, for CNN training, and 15.5X, 4.5X and 69.1X improvements for inference.

The recent advancements in deep learning have demonstrated that inferring high-quality depth maps from a single image has become feasible and accurate, thanks to convolutional neural networks (CNNs), but how to process such compute- and memory-intensive models on portable and low-power devices remains a concern. Dynamic energy-quality scaling is an interesting yet less explored option in this field. It can improve efficiency through opportunistic computing policies where performances are boosted only when needed, achieving on average substantial energy savings. Implementing such a computing paradigm encompasses the availability of a scalable inference model, which is the target of this work. Specifically, we describe and characterize the design of an energy-quality scalable pyramidal network (EQPyD-Net), a lightweight CNN capable of modulating at runtime the computational effort with minimal memory resources. We describe the architecture of the network and the optimization flow, covering the important aspects that enable the dynamic scaling, namely, the optimized training procedures, the compression stage via fixed-point quantization, and the code optimization for the deployment on commercial low-power CPUs adopted in the edge segment. To assess the effect of the proposed design knobs, we evaluated the prediction quality on the standard KITTI data set and the energy and memory resources on the ARM Cortex-A53 CPU. The collected results demonstrate the flexibility of the proposed network and its energy efficiency. EQPyD-Net can be shifted across five operating points, ranging from a maximum accuracy of 82.2% with 0.4 Frame/J and up to 92.6% of energy savings with 6.1% of accuracy loss, still keeping a compact memory footprint of 5.2 MB for the weights and 38.3 MB (in the worst case) for the processing.

Recently, Deep Convolutional Neural Networks (DCNNs) have achieved remarkable progress in computer vision community, including in style transfer tasks. Normally, most methods feed the full image to the DCNN. Although highquality results can be achieved in this manner, several underlying problems arise. For one, with the increase in image resolution, the memory footprint will increase dramatically, leading to high latency and massive power consumption. Furthermore, these methods are usually unable to integrate with the commercial image signal processor (ISP), which processes the image in a line-sequential manner. To solve the above problems, we propose a novel ISP-friendly deep learning-based style transfer algorithm: SequentialStyle. A brand new line-sequential processing mode is proposed, where the image is torn into strips, and each strip is sequentially processed, contributing to less memory demand. We further propose a Spatial-Temporal Synergistic (STS) mechanism that decouples the previously simplex 2-D image style transfer into spatial feature processing (in-strip) and temporal correlation transmission (in-between strips). Compared with the SOTA style transfer algorithms, experimental results show that our SequentialStyle is competitive. Besides, SequentialStyle has less demand for memory consumption, even for the images whose resolutions are 4k or higher.

This paper presents a strategy to accelerate virtually any Poisson solver by taking advantage of s spatial reflection symmetries. More precisely, we have proved the existence of an inexpensive block diagonalisation that transforms the original Poisson equation into a set of 2s fully decoupled subsystems then solved concurrently. This block diagonalisation is identical regardless of the mesh connectivity (structured or unstructured) and the geometric complexity of the problem, therefore applying to a wide range of academic and industrial configurations. In fact, it simplifies the task of discretising complex geometries since it only requires meshing a portion of the domain that is then mirrored implicitly by the symmetries’ hyperplanes. Thus, the resulting meshes naturally inherit the exploited symmetries, and their memory footprint becomes 2s times smaller. Thanks to the subsystems’ better spectral properties, iterative solvers converge significantly faster. Additionally, imposing an adequate grid points’ ordering allows reducing the operators’ footprint and replacing the standard sparse matrix-vector products with the sparse matrixmatrix product, a higher arithmetic intensity kernel. As a result, matrix multiplications are accelerated, and massive simulations become more affordable. Finally, we include numerical experiments based on a turbulent flow simulation and making state-of-theart solvers exploit a varying number of symmetries. On the one hand, algebraic multigrid and preconditioned Krylov subspace methods require up to 23% and 72% fewer iterations, resulting in up to 1.7x and 5.6x overall speedups, respectively. On the other, sparse direct solvers’ memory footprint, setup and solution costs are reduced by up to 48%, 58% and 46%, respectively. This work has been financially supported by two competitive R+D projects: RETOtwin (PDC2021-120970-I00), given by MCIN/AEI/10.13039/501100011033 and European Union Next GenerationEU/PRTR, and FusionCAT (001-P-001722), given by Generalitat de Catalunya RIS3CAT-FEDER. Àdel Alsalti-Baldellou has also been supported by the predoctoral grants DIN2018-010061 and 2019-DI-90, given by MCIN/AEI/10.13039/501100011033 and the Catalan Agency for Management of University and Research Grants (AGAUR), respectively.

International audience; Purpose. Motion-mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design.Methods. A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of nonsmall cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network.Results. The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from urn:x-wiley:00942405:media:mp15347:mp15347-math-0001 to urn:x-wiley:00942405:media:mp15347:mp15347-math-0002 without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from urn:x-wiley:00942405:media:mp15347:mp15347-math-0003 to urn:x-wiley:00942405:media:mp15347:mp15347-math-0004.Conclusion. With 5-s processing time on a mid-range GPU and success rates around urn:x-wiley:00942405:media:mp15347:mp15347-math-0005, the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available.

Tensor decomposition is a promising approach for low-power and real-time application of neural networks on resource-constrained embedded devices. This paper proposes AutoRank, an end-to-end framework for customizing neural network decomposition using cross-layer rank-selection. For many-layer networks, determining the optimal decomposition ranks is a cumbersome task. To overcome this challenge, we establish a state-action-reward system that effectively absorbs inference accuracy and platform specifications into the rank-selection policy. Our proposed framework brings platform characteristics and performance in the customization loop to enable direct incorporation of hardware cost, e.g., runtime and memory footprint. By means of this hardware-awareness, AutoRank customization engine delivers high accuracy decomposed deep neural networks with low execution cost. Our framework minimizes the engineering cost associated with rank selection by providing an automated API for AutoRank that is compatible with popular deep learning libraries and can be readily used by developers.

Real-time rendering of pixel-accurate hard shadows in 3D scenes is still a challenging problem for shadow generation techniques. In this paper, we present the concept of non-overlapping geometric shadow map (NOGSM), which enables the construction of high-quality hard shadows from static lights for any point in the scene. The NOGSM is created in a preprocessing phase, in which the scene’s geometry is looked at from the light’s point of view. All hidden surfaces are removed through total elimination of hidden polygons plus clipping of the partially hidden polygons. The resulting list of non-overlapping projected polygons is then stored in a multi-grid data structure. Our solution generates shadows with far better quality than standard shadow mapping techniques, while maintaining real time performance, and using a smaller memory footprint than compression techniques.

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor. Unfortunately, most standard approaches of this kind rely on hand-tuned parameters, which impair their generalization capabilities and their applicability to real data in the field. In contrast, methods based on deep learning, despite their better generalization, are considered to be too complex to deploy on wearable devices. In this work, we tackle these limitations, proposing a design space exploration methodology to automatically generate a rich family of deep Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a single "seed" model. Our flow involves a cascade of two Neural Architecture Search (NAS) tools and a hardware-friendly quantizer, whose combination yields both highly accurate and extremely lightweight models. When tested on the PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean Absolute Error. Furthermore, we deploy our TCNs on an embedded platform featuring a STM32WB55 microcontroller, demonstrating their suitability for real-time execution. Our most accurate quantized network achieves 4.41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest network that obtains a MAE < 8 BPM, among those generated by our flow, has a memory footprint of 1.9 kB and consumes just 1.79 mJ per inference.

In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches. In particular, we propose to extract multiple sub-networks from a single, untrained neural network by solving an end-to-end optimization task combining differentiable scaling over the original architecture, with multiple regularization terms favouring the diversity of the ensemble. Since our proposal aims to detect and extract sub-structures, we call it Structured Ensemble. On a large experimental evaluation, we show that our method can achieve higher or comparable accuracy to competing methods while requiring significantly less storage. In addition, we evaluate our ensembles in terms of predictive calibration and uncertainty, showing they compare favourably with the state-of-the-art. Finally, we draw a link with the continual learning literature, and we propose a modification of our framework to handle continuous streams of tasks with a sub-linear memory cost. We compare with a number of alternative strategies to mitigate catastrophic forgetting, highlighting advantages in terms of average accuracy and memory., Comment: Article accepted at Neural Networks

Recently, single image super-resolution (SISR), aiming to preserve the lost structural and textural information from the input low resolution image, has witnessed huge demand from the videos and graphics industries. The exceptional success of convolution neural networks (CNNs), has absolutely revolutionized the field of SISR. However, for most of the CNN-based SISR methods, excessive memory consumption in terms of parameters and flops, hinders their application in low-computing power devices. Moreover, different state-of-the-art SR methods collect different features, by treating all the pixels contributing equally to the performance of the network. In this paper, we take into consideration both the performance and the reconstruction efficiency, and propose a Light-weight multi-scale attention residual network (MSAR-Net) for SISR. The proposed MSAR-Net consists of stack of multi-scale attention residual (MSAR) blocks for feature refinement, and an up and down-sampling projection (UDP) block for edge refinement of the extracted multi-scale features. These blocks are capable of effectively exploiting the multi-scale edge information, without increasing the number of parameters. Specially, we design our network in progressive fashion, for substituting the large scale factors ( × 4) combinations, with small scale factor ( × 2) combinations, and thus gradually exploit the hierarchical information. In parallel, for modulation of multi-scale features in global and local manners, channel and spatial attention in MSAR block is being used. Visual results and quantitative metrics of PSNR and SSIM exhibit the accuracy of the proposed approach on synthetic benchmark super-resolution datasets. The experimental analysis shows that the proposed approach outperforms the other existing methods for SISR in terms of memory footprint, inference time and visual quality.

Deep neural networks (DNNs) are being incorporated in resource-constrained IoT devices, which typically rely on reduced memory footprint and low-performance processors. While DNNs’ precision and performance can vary and are essential, it is also vital to deploy trained models that provide high reliability at low cost. To achieve an unyielding reliability and safety level, it is imperative to provide electronic computing systems with appropriate mechanisms to tackle soft errors. This paper, therefore, investigates the relationship between soft errors and model accuracy. In this regard, an extensive soft error assessment of the MobileNet model is conducted considering precision bitwidth variations (2, 4, and 8 bits) running on an Arm Cortex-M processor. In addition, this work promotes the use of a register allocation technique (RAT) that allocates the critical DNN function/layer to a pool of specific general-purpose processor registers. Results obtained from more than 4.5 million fault injections show that RAT gives the best relative performance, memory utilization, and soft error reliability trade-offs w.r.t. a more traditional replication-based approach. Results also show that the MobileNet soft error reliability varies depending on the precision bitwidth of its convolutional layers.

Processing and analyzing time series datasets have become a central issue in many domains requiring data management systems to support time series as a native data type. A core access primitive of time series is matching, which requires efficient algorithms on-top of appropriate representations like the symbolic aggregate approximation (SAX) representing the current state of the art. This technique reduces a time series to a low-dimensional space by segmenting it and discretizing each segment into a small symbolic alphabet. Unfortunately, SAX ignores the deterministic behavior of time series such as cyclical repeating patterns or a trend component affecting all segments, which may lead to a sub-optimal representation accuracy. We therefore introduce a novel season- and a trend-aware symbolic approximation and demonstrate an improved representation accuracy without increasing the memory footprint. Most importantly, our techniques also enable a more efficient time series matching by providing a match up to three orders of magnitude faster than SAX.

We propose an innovative fast 3D approach for the accurate computation of electromagnetic losses in the metallic armors of submarine cables. In order to develop a scheme that is more efficient with respect to most commonly used 3D simulation methods, typically based on the Finite Element Method (FEM), we proceed by proposing a suitable discretization of an integral formulation. In the proposed approach, each wire of the armor is modeled as filamentary which leads to a dramatic reduction in the number of degrees of freedom in the numerical model and in the overall computational burden. The new approach can be applied to cables where armor wires are stranded either with opposite (contralay) or same (equilay) orientation as the central phase cables. The efficiency of the proposed method is especially notable in latter case for which FEM is very demanding due to the extremely large model size. The reduction in both computation times and memory footprint allows performing extensive sensitivity studies with respect to geometrical parameters and material properties that would be otherwise unaffordable with existing 3D methods.