Poids | EPFL Graph Search

Le poids est la force de la pesanteur, d'origine gravitationnelle et inertielle, exercée, par exemple, par la Terre sur un corps massique en raison uniquement du voisinage de la Terre. Son unité dans le Système international est le newton. Il est égal à l'opposé de la résultante des autres forces appliquées au centre de gravité du corps lorsque celui-ci est immobile dans le référentiel terrestre. Cette force est la résultante des efforts dus à la gravité et à la force d'inertie d'entraînement due à la rotation de la Terre sur elle-même. Elle s'applique au centre de gravité du corps et sa direction définit la verticale qui passe approximativement par le centre de la Terre. Le poids est une action à distance toujours proportionnelle à la masse. Le poids ne prend en compte que les forces de gravitation et d'inertie d'entraînement dues à la masse et à la rotation de la Terre. Quand on prend également en compte d'autres forces telles que la poussée d'Archimède, d'autres forces de gravit

An Adaptive Landing Gear for Extending the Operational Range of Helicopters

Tobias Löw

Conventional skid or wheel based helicopter landing gears severely limit off-field landing possibilities, which are crucial when operating in scenarios such as mountain rescue. In this context, slopes beyond 8° and small obstacles can already pose a substantial hazard. An adaptive landing gear is proposed to overcome these limitations. It consists of four legs with one degree of freedom each. The total weight was minimized to demonstrate economic practicability. This was achieved by an innovative actuation, composed of a parallel arrangement of motor and brake, which relieves the motor from large impact loads during hard landings. The loads are alleviated by a spring-damper system acting in series to the actuation. Each leg is individually force controlled for optimal load distribution on compliant ground and to avoid tipping. The operation of the legs is fully autonomous during the landing phase. A prototype was designed and successfully tested on an unmanned helicopter with a maximum take-off weight of 78 kg. Finally, the implementation of the landing gear concept on aircraft of various scales was discussed.

IEEE2019

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Giorgio Cristiano, Massimo Giordano

Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)-such as Phase-Change Memory or Resistive Memory-can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computation occurs in the analog domain at the location of weight data encoded into the conductances of the NVM devices. This allows DNN training of fully-connected layers to be performed faster and with less energy. Using a mixed-hardware-software experiment, we recently showed that by encoding each weight into four distinct physical devices-a "Most Significant Conductance" pair (MSP) and a "Least Significant Conductance" pair (LSP)-we can train DNNs to software-equivalent accuracy despite the imperfections of real analog memory devices. We surmised that, by dividing the task of updating and maintaining weight values between the two conductance pairs, this approach should significantly relax the otherwise quite stringent device requirements. In this paper, we quantify these relaxed requirements for analog memory devices exhibiting a saturating conductance response, assuming either an immediate or a delayed steep initial slope in conductance change. We discuss requirements on the LSP imposed by the "Open Loop Tuning" performed after each training example and on the MSP due to the "Closed Loop Tuning" performed periodically for weight transfer between the conductance pairs. Using simulations to evaluate the final generalization accuracy of a trained four-neuronlayer fully-connected network, we quantify the required dynamic range (as controlled by the size of the steep initial jump), the tolerable device-to-device variability in both maximum conductance and maximum conductance change, the tolerable pulse-to-pulse variability in conductance change, and the tolerable device yield, for both the LSP and MSP devices. We also investigate various Closed Loop Tuning strategies and describe the impact of the MSP/LSP approach on device endurance. Published by AIP Publishing.

2018

Constructive Training Methods for Feedforward Neural Networks with Binary Weights

Frédéric Aviolat

Quantization of the parameters of a Perceptron is a central problem in hardware implementation of neural networks using a numerical technology. A neural model with each weight limited to a small integer range will require little surface of silicon. Moreover, according to Occam's razor principle, better generalization abilities can be expected from a simpler computational model. The price to pay for these benefits lies in the difficulty to train these kind of networks. This paper proposes essentially two new ideas for constructive training algorithms, and demonstrates their efficiency for the generation of feedforward networks composed of Boolean threshold gates with discrete weights. A proof of the convergence of these algorithms is given. Some numerical experiments have been carried out and the results are presented in terms of the size of the generated networks and of their generalization abilities.

1996

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Giorgio Cristiano, Massimo Giordano

2018