Energy minimization | EPFL Graph Search

In the field of computational chemistry, energy minimization (also called energy optimization, geometry minimization, or geometry optimization) is the process of finding an arrangement in space of a collection of atoms where, according to some computational model of chemical bonding, the net inter-atomic force on each atom is acceptably close to zero and the position on the potential energy surface (PES) is a stationary point (described later). The collection of atoms might be a single molecule, an ion, a condensed phase, a transition state or even a collection of any of these. The computational model of chemical bonding might, for example, be quantum mechanics. As an example, when optimizing the geometry of a water molecule, one aims to obtain the hydrogen-oxygen bond lengths and the hydrogen-oxygen-hydrogen bond angle which minimize the forces that would otherwise be pulling atoms together or pushing them apart. The motivation for performing a geometry optimization is the physica

Extension d'algorithmes d'ordre zéro pour l'optimisation stochastique

Pierre Huguenin

The electrical discharge machining process (EDM) was discovered in the 1950s, and was then used essentially to destroy unrecoverable damaged screws. Since then, huge progress has been achieved in making this process reliable and able to perform the most complex machining operations on the most sophisticated materials. Two main processes use electrical discharge machining. First, Die Sinking EDM (DSEDM) in which an electrode, moved along a usually vertical axis, makes an imprint into a mechanical element ; second, Wire EDM (WEDM), uses a wire as an electrode and makes it possible to perform cut operations. Two specific aspects of the EDM process make it particularly challenging for optimization. First, the process evolves with the machining position. In the DSEDM process, where the electrode sinks deeply into the material, the fragments spawn by erosion (contamination) are trapped, thus modifying the sparking conditions. In the WEDM process, the main factors that drive the evolution of the process are the machining operations. Second, the measurements are very noisy, which is due to underlying, mainly random, physical phenomenon ; this is particularly true to spark triggering. The process evolution influences the single criterion to be minimized : total machining time. However, the latter is only known once the operations are completed. As a result, the whole history of manipulated variables influences the final criterion to minimize in this case of dynamic optimization. A major contribution of this work is a proof that a first-order model of the DSEDM process, with the machining position as a state variable, makes it possible to transform a dynamic optimization problem into a static optimization problem. The tools used in this demonstration are Pontryagin's Minimum Principle as well as Parametric Programming. The conclusion is that in order to achieve minimum machining time, maximum speed must be sought all along the trajectory. The online search for maximum speed is another important contribution of this thesis. Noisy efficiency functions are, indeed, known to be a significant challenge to the reliability of optimization algorithms. To address this issue the Golden Ratio Search and the Nelder-Mead Simplex algorithms were chosen as the starting point, as they do not rely explicitly on the gradient of the efficiency function. The addition of a further dilation condition makes these algorithms more effective in stochastic mode. This condition is based on the detection of contradictory measurement samples as compared to the shape of the efficiency function, which is assumed to be unimodal. As a result of this modification, the density of the final optimization points is well centred relative to the theoretical optimum, and dispersion is small. Moreover, the size of the search region for both algorithms never approaches zero. Consequently, as the machining conditions evolve, the optimizer can target a new optimum. This adaptability proves to be a significant improvement over existing algorithms. In the case of the DSEDM process, a simple model of the efficiency surface as a function of the control variables, which has been calibrated on sample measures, has allowed for a validation of the static optimization. For the WEDM process, conclusive results for the modified algorithms have been obtained both by simulation and during machine-based tests.

EPFL2005

Synthesis of Flexible Accelerators for Early Adoption of Ring-LWE Post-quantum Cryptography

Subhadeep Banik, Hamid Nejatollahi, Francesco Regazzoni

The advent of the quantum computer makes current public-key infrastructure insecure. Cryptography community is addressing this problem by designing, efficiently implementing, and evaluating novel public-key algorithms capable of withstanding quantum computational power. Governmental agencies, such as NIST, are promoting standardization of quantum-resistant algorithms that is expected to run for 7 years. Several modern applications must maintain permanent data secrecy; therefore, they ultimately require the use of quantum-resistant algorithms. Because algorithms are still under scrutiny for eventual standardization, the deployment of the hardware implementation of quantum-resistant algorithms is still in early stages. In this article, we propose a methodology to design programmable hardware accelerators for lattice-based algorithms, and we use the proposed methodology to implement flexible and energy efficient post-quantum cache-based accelerators for NewHope, Kyber, Dilithium, Key Consensus from Lattice (KCL), and R.EMBLEM submissions to the NIST standardization contest. To the best of our knowledge, we propose the first efficient domain-specific, programmable cache-based accelerators for lattice-based algorithms. We design a single accelerator for a common kernel among various schemes with different kernel sizes, i.e., loop count, and data types. This is in contrast to the traditional approach of designing one special purpose accelerators for each scheme. We validate our methodology by integrating our accelerators into an HLS-based SoC infrastructure based on the X86 processor and evaluate overall performance. Our experiments demonstrate the suitability of the approach and allow us to collect insightful information about the performance bottlenecks and the energy efficiency of the explored algorithms. Our results provide guidelines for hardware designers, highlighting the optimization points to address for achieving the highest energy minimization and performance increase. At the same time, our proposed design allows us to specify and execute new variants of lattice-based schemes with superior energy efficiency compared to the main application processor without changing the hardware acceleration platform. For example, we manage to reduce the energy consumption up to 2.1x and energy-delay product (EDP) up to 5.2x and improve the speedup up to 2.5x.

ASSOC COMPUTING MACHINERY2020

Conditional Random Fields for Multi-Camera Object Detection

Horesh Beny Ben Shitrit, Xavier Boix Bosch, Pascal Fua, Gemma Roig Noguera

We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account different object classes simultaneously. Given several images of the scene taken from different angles, our system estimates the ground plane location of the objects from the output of several object detectors applied at each viewpoint. We cast the problem as an energy minimization modeled with a Conditional Random Field (CRF). Instead of predicting the presence of an object at each image location independently, we simultaneously predict the labeling of the entire scene. Our CRF is able to take into account occlusions between objects and contextual constraints among them. We propose an effective iterative strategy that renders tractable the underlying optimization problem, and learn the parameters of the model with the max-margin paradigm. We evaluate the performance of our model on several challenging multi-camera pedestrian detection datasets namely PETS 2009 and EPFL terrace sequence. We also introduce a new dataset in which multiple classes of objects appear simultaneously in the scene. It is here where we show that our method effectively handles occlusions in the multi-class case.