On the Efficiency of Sparse-Tiled Tensor Graph Processing for Low Memory Usage

The memory space taken to host and process large tensor graphs is a limiting factor for embedded ConvNets. Even though many data-driven compression pipelines have proven their efficacy, this work shows there is still room for optimization at the intersection with compute-oriented optimizations. We demonstrate that tensor pruning via weight sparsification can cooperate with a model-agnostic tiling strategy, leading ConvNets towards a new feasible region of the solution space. The collected results show for the first time fast versions of MobileNets deployed at full scale on an ARM M7 core with 512KB of RAM and 2MB of FLASH memory

Dataflow Restructuring for Active Memory Reduction in Deep Neural Networks

The volume reduction of the activation maps produced by the hidden layers of a Deep Neural Network (DNN) is a critical aspect in modern applications as it affects the on-chip memory utilization, the most limited and costly hardware resource. Despite the availability of many compression methods that leverage the statistical nature of deep learning to approximate and simplify the inference model, e.g., quantization and pruning, there is room for deterministic optimizations that instead tackle the problem from a computational view. This work belongs to this latter category as it introduces a novel method for minimizing the active memory footprint. The proposed technique, which is data-, model-, compiler-, and hardware-agnostic, does implement a functional-preserving, automated graph restructuring where the memory peaks are suppressed and distributed over time, leading to flatter profiles with less memory pressure. Results collected on a representative class of Convolutional DNNs with different topologies, from Vgg16 and SqueezeNetV1.1 to the recent MobileNetV2, ResNet18, and InceptionV3, provide clear evidence of applicability, showing remarkable memory savings (62.9% on average) with low computational overhead (8.6% on average)

Design of a Stationary Energy Recovery System in Rail Transport

Although rail is one of the most sustainable transport systems, there is still room to reduce its energy demand. In particular, during the braking of DC powered trains, a significant amount of energy is wasted. The recent developments in energy storage system technologies, combined with the widely used technique of regenerative braking, can considerably increase energy saving. This paper explores this theme, quantifying the amount of braking energy that can be potentially recovered in a real case study, starting from the experimental data measured on-board train. A simplified numerical model of the recovery process has been implemented. Adopting it, the energy that can be saved, with one or two energy storage systems, has been quantified for each possible position along the track. The procedure allows to determine the optimal position. Further findings about the impact of voltage level on the efficiency of the recovery process have been reported. The optimal level of voltage has been determined, also considering the additional losses in the catenary, both during the traction and braking phase of the train. Moreover, it allows dimensioning of stationary storage systems considering two different energy management strategies and their impact on the peak of stored energy. The proposed approach will be presented with reference to the concrete case of a specific route on the Italian rail network, analyzing a train in normal commuter service and the obtained results will be discussed. In the best situation, about the 73% of the braking energy can be recovered

Unimpaired Neuropsychological Performance and Enhanced Memory Recall in Patients with Sbma: A Large Sample Comparative Study.

Peculiar cognitive profile of patients with SBMA has been described by fragmented literature. Our retrospective study reports the neuropsychological evaluations of a large cohort of patients in order to contribute towards the understanding of this field. We consider 64 neuropsychological evaluations assessing mnesic, linguistic and executive functions collected from 2013 to 2015 in patients attending at Motor Neuron Disease Centre of University of Padova. The battery consisted in: Digit Span forwards and backwards, Prose Memory test, Phonemic Verbal fluency and Trail making tests. ANCOVA statistics were employed to compare tests scores results with those obtained from a sample of healthy control subjects. Multiple linear regressions were used to study the effect on cognitive performance of CAG-repeat expansion, the degree of androgen insensitivity and their interaction to cognitive performance. Statistical analyses did not reveal altered scores in any neuropsychological tests among those adopted. Interestingly, patients performed significantly better in the Prose Memory test's score. No relevant associations were found with genetic, hormonal or clinical patients' profile. Results inconsistent with previous studies have been interpreted according to the phenomenon of somatic mosaicism. We suggest a testosterone-related and the mood state-dependant perspectives as two possible interpretations of the enhanced performances in the Prose Memory test. Further studies employing more datailed tests batteries are encouraged

Effects of Orthogonal Rotating Electric Fields on Electrospinning Process

Electrospinning is a nanotechnology process whereby an external electric field is used to accelerate and stretch a charged polymer jet, so as to produce fibers with nanoscale diameters. In quest of a further reduction in the cross section of electrified jets hence of a better control on the morphology of the resulting electrospun fibers, we explore the effects of an external rotating electric field orthogonal to the jet direction. Through extensive particle simulations, it is shown that by a proper tuning of the electric field amplitude and frequency, a reduction of up to a $30 \%$ in the aforementioned radius can be obtained, thereby opening new perspectives in the design of future ultra-thin electrospun fibres. Applications can be envisaged in the fields of nanophotonic components as well as for designing new and improved filtration materials.Comment: 22 pages, 8 figure

Enabling monocular depth perception at the very edge

Depth estimation is crucial in several computer vision applications, and a recent trend aims at inferring such a cue from a single camera through computationally demanding CNNs - precluding their practical deployment in several application contexts characterized by low-power constraints. Purposely, we develop a tiny network tailored to microcontrollers, processing low-resolution images to obtain a coarse depth map of the observed scene. Our solution enables depth perception with minimal power requirements (a few hundreds of mW), accurately enough to pave the way to several high-level applications at-the-edge

Measuring the impact of reversible substations on energy efficiency in rail transport

open6Nowadays great interest is placed on the environmental issue. Greenhouse gas emissions are more than 50% higher than in 1990. European energy policy has been supporting efficient energy management in order to reduce the railway transport emissions by 50% within 2030. The railway stakeholders are encouraged to adopt technological solutions to foster energy efficiency. The electrodynamic braking combined with the adoption of reversible substations is one of the most promising solutions. In order to evaluate the impact of this innovative technology, a measurement campaign has been conducted on Metro de Madrid where a reversible substation was installed. In this paper, a preliminary analysis on the data acquired is presented. Traceable and accurate on-board train measurements of the energy flows and the losses are fundamental to quantify the impact of these new technologies and to carry out a survey on the efficiencies of the different vehicle components and on the strategies to reduce the energy consumption in the various operation modes. © IMEKO TC-4 2020.openCascetta F., Cipolletta G., Delle Femine A., Gallo D., Giordano D., Signorino D.Cascetta, F.; Cipolletta, G.; Delle Femine, A.; Gallo, D.; Giordano, D.; Signorino, D