Weight | EPFL Graph Search

In science and engineering, the weight of an object is the force acting on the object due to acceleration or gravity. Some standard textbooks define weight as a vector quantity, the gravitational force acting on the object. Others define weight as a scalar quantity, the magnitude of the gravitational force. Yet others define it as the magnitude of the reaction force exerted on a body by mechanisms that counteract the effects of gravity: the weight is the quantity that is measured by, for example, a spring scale. Thus, in a state of free fall, the weight would be zero. In this sense of weight, terrestrial objects can be weightless: so if one ignores air resistance, one could say the legendary apple falling from the tree, on its way to meet the ground near Isaac Newton, was weightless. The unit of measurement for weight is that of force, which in the International System of Units (SI) is the newton. For example, an object with a mass of one kilogram has a weight of about 9.8 newtons o

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

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

Giorgio Cristiano, Massimo Giordano

2018