LALP: a language to program custom FPGA-based acceleration engines

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly important in embedded and high-performance computing systems. They allow performance levels close to the ones obtained with Application-Specific Integrated Circuits, while still keeping design and implementation flexibility. However, to efficiently program FPGAs, one needs the expertise of hardware developers in order to master hardware description languages (HDLs) such as VHDL or Verilog. Attempts to furnish a high-level compilation flow (e.g., from C programs) still have to address open issues before broader efficient results can be obtained. Bearing in mind an FPGA available resources, it has been developed LALP (Language for Aggressive Loop Pipelining), a novel language to program FPGA-based accelerators, and its compilation framework, including mapping capabilities. The main ideas behind LALP are to provide a higher abstraction level than HDLs, to exploit the intrinsic parallelism of hardware resources, and to allow the programmer to control execution stages whenever the compiler techniques are unable to generate efficient implementations. Those features are particularly useful to implement loop pipelining, a well regarded technique used to accelerate computations in several application domains. This paper describes LALP, and shows how it can be used to achieve high-performance computing solutions.

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Flow Boiling Characteristics for R1234ze(E) in 1.0 and 2.2 mm Circular Channels

Experimental flow boiling heat transfer results are presented for horizontal 1.0 and 2.2 mm I. D. (internal diameter) stainless steel tubes for tests with R1234ze(E), a new refrigerant developed as a substitute for R134a with a much lower global warming potential (GWP). The experiments were performed for these two tube diameters in order to investigate a possible transition between macro and microscale flow boiling behavior. The experimental campaign includes mass velocities ranging from 50 to 1500 kg/m(2) s, heat fluxes from 10 to 300 kW/m(2), exit saturation temperatures of 25, 31 and 35 degrees C, vapor qualities from 0.05 to 0.99 and heated lengths of 180 mm and 361 mm. Flow pattern characterization was performed using high speed videos. Heat transfer coefficient, critical heat flux and flow pattern data were obtained. R1234ze(E) demonstrated similar thermal performance to R134a data when running at similar conditions. [DOI: 10.1115/1.4004933]