Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms

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A new family of blind adaptive equalization algorithms

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Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service

In the near future FPGAs will be available by the hour, however this new Infrastructure as a Service (IaaS) usage mode presents both an opportunity and a challenge: The opportunity is that programmers can potentially trade resources for performance on a much larger scale, for much shorter periods of time than before. The challenge is in finding and traversing the trade-off for heterogeneous IaaS that guarantees increased resources result in the greatest possible increased performance. Such a trade-off is Pareto optimal. The Pareto optimal trade-off for clusters of heterogeneous resources can be found by solving multiple, multi-objective optimisation problems, resulting in an optimal allocation of tasks to the available platforms. Solving these optimisation programs can be done using simple heuristic approaches or formal Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up to 110% faster than a heuristic approach, and over 50% cheaper. These results suggest that high quality performance-resource trade-offs of heterogeneous IaaS are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software Programmers (FSP 2015) (arXiv:1508.06320

Computer Architectures to Close the Loop in Real-time Optimization

© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other

Junior Must Pay: Pricing the Implicit Put in Privatizing Social Security

Proposals that portion of the Social Security Trust Fund assets be invested in equities entail the possibility that a severe decline in equity prices renders the Fund assets insufficient to provide the currently mandated level of benefits. In this event, existing taxpayers may be compelled to act as insurers of last resort. The cost to taxpayers of such an implicit commitment equals the value of a put option with payoff equal to the benefit's shortfall. We calibrate an OLG model that generates realistic equity premia and value the put. With 20 percent of the Fund assets invested in equities, the highest level currently under serious discussion, we value a put that guarantees the currently mandated level of benefits at one percent of GDP, or a temporary increase in Social Security taxation of at most 25 percent. We value a put that guarantees 90 percent of benefits at merely .03 percent of GDP. In contrast to earlier literature, our results account for the significant changes in the distribution of security returns resulting from Trust Fund purchases. We also explore the inter-generational welfare implications of the guarantee.

Junior is Rich: Bequests as Consumption

We explore the consequences for asset pricing of admitting a bequest motive into an otherwise standard overlapping generations model where agents trade equity and perpetual debt securities. Prices of securities are seen to be approximately 50% higher in an economy with bequests as compared to an otherwise identical one where bequests are absent. Robust estimates of the equity premium are obtained in several cases where the desire to leave bequests is modest relative to the desire for old age consumption.

Sliding window adaptive fast QR and QR-lattice algorithms

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Residual echo signal in critically sampled subband acoustic echo cancellers based on IIR and FIR filter banks

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High-level synthesis using the Julia language

The growing proliferation of FPGAs and High-level Synthesis (HLS) tools has led to a large interest in designing hardware accelerators for complex operations and algorithms. However, existing HLS toolflows typically require a significant amount of user knowledge or training to be effective in both industrial and research applications. In this paper, we propose using the Julia language as the basis for an HLS tool. The Julia HLS tool aims to decrease the barrier to entry for hardware acceleration by taking advantage of the readability of the Julia language and by allowing the use of the existing large library of standard mathematical functions written in Julia. We present a prototype Julia HLS tool, written in Julia, that transforms Julia code to VHDL. We highlight how features of Julia and its compiler simplified the creation of this tool, and we discuss potential directions for future work

Nonlinear predictive control on a heterogeneous computing platform

Nonlinear Model Predictive Control (NMPC) is an advanced control technique that often relies on computationally demanding optimization and integration algorithms. This paper proposes and investigates a heterogeneous hardware implementation of an NMPC controller based on an interior point algorithm. The proposed implementation provides flexibility of splitting the workload between a general-purpose CPU with a fixed architecture and a field-programmable gate array (FPGA) to trade off contradicting design objectives, namely performance and computational resource usage. A new way of exploiting the structure of the Karush-Kuhn-Tucker (KKT) matrix yields significant memory savings, which is crucial for reconfigurable hardware. For the considered case study, a 10x memory savings compared to existing approaches and a 10x speedup over a software implementation are reported. The proposed implementation can be tested from Matlab using a new release of the Protoip software tool, which is another contribution of the paper. Protoip abstracts many low-level details of heterogeneous hardware programming and allows quick prototyping and processor-in-the-loop verification of heterogeneous hardware implementations