BDDs (Binary Decision Diagrams) are often used to represent Boolean expressions in hardware synthesis, hardware and software verification and numerous other applications. BDD computation, implemented using tree data structures with binary nodes, is inherently memory intensive, and therefore suffers from the von Neumann memory bottleneck. This thesis examines an approach which can speed up BDD c...

Methods exhibiting linear scaling with respect to the size of the system, so called O(N) methods, are an essential tool for the calculation of the electronic structure of large systems containing many atoms. They are based on algorithms which take advantage of the decay properties of the density matrix. In this article the physical decay properties of the density matrix will therefore first be ...

Traditional discrete-event packet-level approaches to simulating computer networks become computationally infeasible as the number of network nodes or their complexity increases. An alternative approach, in which packet-level traffic sources are replaced by fluid sources, has been proposed to address this challenge. In this paper we compare the amount of computational effort needed to simulate ...

The adoption of register-transfer level (RTL) sign-off in ASIC design methodologies, and the increasing scale of system-on-chip integration, are leading to unprecedented accuracy and efficiency demands on RT-level estimation tools. In this work, we focus on the deployment of a simulation-based RTL power estimation tool in a commercial design flow, and describe several enhancements that improve ...

y(t0) = y0 Here f(t, y) is a given function, t0 is a given initial time and y0 is a given initial value for y. The unknown in the problem is the function y(t). Two obvious considerations in deciding whether or not a given algorithm is of any practical value are (a) the amount of computational effort required to execute the algorithm and (b) the accuracy that this computational effort yields. Fo...

Recent theoretical models suggest that motor learning includes at least two processes: error minimization and memory decay. While learning a novel movement, a motor memory of the movement is gradually formed to minimize the movement error between the desired and actual movements in each training trial, but the memory is slightly forgotten in each trial. The learning effects of error minimizatio...

Does how much an agent thinks about its own actions affect the global properties of a system? We use the El Farol Bar Problem to investigate this question. In this model, the El Farol Bar represents a scarce resource. Does the amount of computational ability that agents possess affect resource utilization? For instance, if agents attend the bar randomly on average 50 people will go to the bar. ...

Optimal decision making mandates organisms learn the relevant features of choice options. Likewise, knowing how much effort we should expend can assume paramount importance. A mesolimbic network supports reward learning, but it is unclear whether other choice features, such as effort learning, rely on this same network. Using computational fMRI, we show parallel encoding of effort and reward pr...

I derive the equation of motion in molecular dynamics for doing full lattice QCD simulations with clover quarks. The even-odd preconditioning technique, expected to significantly reduce the computational effort, is further developed for the simulations.