Pixie: A heterogeneous Virtual Coarse-Grained Reconfigurable Array for high performance image processing applications

Coarse-Grained Reconfigurable Arrays (CGRAs) enable ease of programmability and result in low development costs. They enable the ease of use specifically in reconfigurable computing applications. The smaller cost of compilation and reduced reconfiguration overhead enables them to become attractive platforms for accelerating high-performance computing applications such as image processing. The CGRAs are ASICs and therefore, expensive to produce. However, Field Programmable Gate Arrays (FPGAs) are relatively cheaper for low volume products but they are not so easily programmable. We combine best of both worlds by implementing a Virtual Coarse-Grained Reconfigurable Array (VCGRA) on FPGA. VCGRAs are a trade off between FPGA with large routing overheads and ASICs. In this perspective we present a novel heterogeneous Virtual Coarse-Grained Reconfigurable Array (VCGRA) called "Pixie" which is suitable for implementing high performance image processing applications. The proposed VCGRA contains generic processing elements and virtual channels that are described using the Hardware Description Language VHDL. Both elements have been optimized by using the parameterized configuration tool flow and result in a resource reduction of 24% for each processing elements and 82% for each virtual channels respectively.Comment: Presented at 3rd International Workshop on Overlay Architectures for FPGAs (OLAF 2017) arXiv:1704.0880

Frictional Duality Observed during Nanoparticle Sliding

One of the most fundamental questions in tribology concerns the area dependence of friction at the nanoscale. Here, experiments are presented where the frictional resistance of nanoparticles is measured by pushing them with the tip of an atomic force microscope. We find two coexisting frictional states: While some particles show finite friction increasing linearly with the interface areas of up to 310,000nm^2, other particles assume a state of frictionless sliding. The results further suggest a link between the degree of surface contamination and the occurrence of this duality.Comment: revised versio

A Tight O(4k/pc)O(4^k/p_c) Runtime Bound for a (μ\mu+1) GA on Jumpk_k for Realistic Crossover Probabilities

The Jump$_k$ benchmark was the first problem for which crossover was proven to give a speedup over mutation-only evolutionary algorithms. Jansen and Wegener (2002) proved an upper bound of $O({\rm poly}(n) + 4^k/p_c)$ for the ($\mu$+1)~Genetic Algorithm ($(\mu+1)$ GA), but only for unrealistically small crossover probabilities $p_c$. To this date, it remains an open problem to prove similar upper bounds for realistic~$p_c$; the best known runtime bound for $p_c = \Omega(1)$ is $O((n/\chi)^{k-1})$, $\chi$ a positive constant. Using recently developed techniques, we analyse the evolution of the population diversity, measured as sum of pairwise Hamming distances, for a variant of the \muga on Jump$_k$. We show that population diversity converges to an equilibrium of near-perfect diversity. This yields an improved and tight time bound of $O(\mu n \log(k) + 4^k/p_c)$ for a range of~$k$ under the mild assumptions $p_c = O(1/k)$ and $\mu \in \Omega(kn)$. For all constant~$k$ the restriction is satisfied for some $p_c = \Omega(1)$. Our work partially solves a problem that has been open for more than 20 years.Comment: Long version of the paper which appears at GECCO 202

Analysing Equilibrium States for Population Diversity

Population diversity is crucial in evolutionary algorithms as it helps with global exploration and facilitates the use of crossover. Despite many runtime analyses showing advantages of population diversity, we have no clear picture of how diversity evolves over time. We study how population diversity of $(\mu+1)$ algorithms, measured by the sum of pairwise Hamming distances, evolves in a fitness-neutral environment. We give an exact formula for the drift of population diversity and show that it is driven towards an equilibrium state. Moreover, we bound the expected time for getting close to the equilibrium state. We find that these dynamics, including the location of the equilibrium, are unaffected by surprisingly many algorithmic choices. All unbiased mutation operators with the same expected number of bit flips have the same effect on the expected diversity. Many crossover operators have no effect at all, including all binary unbiased, respectful operators. We review crossover operators from the literature and identify crossovers that are neutral towards the evolution of diversity and crossovers that are not.Comment: To appear at GECCO 202

Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation

Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multiobjective evolutionary optimisation (EMO) is still in its early development. Fundamental questions such as the benefits of the crossover operator are still not fully understood. We provide a theoretical analysis of the well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover: we propose classes of "royal road" functions on which these algorithms cover the whole Pareto front in expected polynomial time if crossover is being used. But when disabling crossover, they require exponential time in expectation to cover the Pareto front. The latter even holds for a large class of black-box algorithms using any elitist selection and any unbiased mutation operator. Moreover, even the expected time to create a single Pareto-optimal search point is exponential. We provide two different function classes, one tailored for one-point crossover and another one tailored for uniform crossover, and we show that immune-inspired hypermutations cannot avoid exponential optimisation times. Our work shows the first example of an exponential performance gap through the use of crossover for the widely used NSGA-II algorithm and contributes to a deeper understanding of its limitations and capabilities.Comment: This is a significant extension of the previous version. We extend the results to uniform crossover and also investigate effects of hypermutation. The previous version is available both on arXiv (arXiv:2301.13687v1) and in AAAI Publications (https://ojs.aaai.org/index.php/AAAI/article/view/26460

Tire-road noise: an experimental study of tire and road design parameters

It is widely known that road traffic noise has negative influences on human health. Hence, as tire-road noise is considered to be the most dominant cause of road traffic noise above 30-50 km/h, a lot of research is performed by the two involving industries: road authorities/manufacturers and tire manufacturers. Usually, the parameters influencing exterior tire-road noise are often examined separately, whereas it is the tire-road interaction which obviously causes the actual noise. An integral approach, i.e. assessing possible measures to reduce tire-road noise from both the road and the tire point of view, is needed to further reduce traffic noise. In a project Silent Safe Traffic, this tire-road interaction is studied in more detail without focusing on either tire or road but looking at the tire-road system. In this publication we present experimental results of tire and road design parameters influencing tire-road noise from a fixed reference tire-road configuration. The influence of tire tread pattern, compound and construction as well as the influence of road roughness, acoustic absorption and driving speed on the exterior tire-road noise, measured by a CPX-set up, is reported. Keywords: Tire, Road, Measuremen

An algebraic framework to represent finite state machines in single-layer recurrent neural networks

In this paper we present an algebraic framework to represent finite state machines (FSMs) in single-layer recurrent neural networks (SLRNNs), which unifies and generalizes some of the previous proposals. This framework is based on the formulation of both the state transition function and the output function of an FSM as a linear system of equations, and it permits an analytical explanation of the representational capabilities of first-order and higher-order SLRNNs. The framework can be used to insert symbolic knowledge in RNNs prior to learning from examples and to keep this knowledge while training the network. This approach is valid for a wide range of activation functions, whenever some stability conditions are met. The framework has already been used in practice in a hybrid method for grammatical inference reported elsewhere (Sanfeliu and Alquézar 1994).Peer Reviewe

<x>_{u-d} from lattice QCD at nearly physical quark masses

We determine the second Mellin moment of the isovector quark parton distribution function _{u-d} from lattice QCD with N_f=2 sea quark flavours, employing the non-perturbatively improved Wilson-Sheikholeslami-Wohlert action at a pseudoscalar mass of 157(6) MeV. The result is converted non-perturbatively to the RI'-MOM scheme and then perturbatively to the MSbar scheme at a scale mu = 2 GeV. As the quark mass is reduced we find the lattice prediction to approach the value extracted from experiments.Comment: 4 pages, 3 figures, v2: minor updates including journal ref