15339123
Utilizing jump flooding in image-based soft shadows
This paper studies the usage of the GPU as a collection of groups of related processing units, where each group communicates in some way to complete a computation efficiently and effectively. In particular, we use the GPU to perform jump flooding to pass information among groups of processing units in the design of two simple real-time soft shadow algorithms. These two algorithms are purely image-based in generating plausible soft shadows. Their computational costs depend mainly on the resolution of the shadow map or the screen. They run on an order of magnitude faster than existing comparable methods.
9297752
Edge insertion for optimal triangulations
Edge insertion iteratively improves a triangulation of a finite point set in ℜ2 by adding a new edge, deleting old edges crossing the new edge, and retriangulating the polygonal regions on either side of the new edge. This paper presents an abstract view of the edge insertion paradigm, and then shows that it gives polynomial-time algorithms for several types of optimal triangulations, including minimizing the maximum slope of a piecewise-linear interpolating surface.
7171205
An optimal bound for high-quality conforming triangulations
AbstractThis paper shows that, for any plane geometric graph
$$\mathcal{G}$$
 withn vertices, there is a triangulation
$$\mathcal{T}$$
 that conforms to
$$\mathcal{G}$$
, i.e., each edge of
$$\mathcal{G}$$
 is the union of some edges of
$$\mathcal{T}$$
, where
$$\mathcal{T}$$
 hasO(n2) vertices with each angle of its triangles measuring no more than 11/15π. Additionally,
$$\mathcal{T}$$
 can be computed inO(n2 logn) time.
1231228
Font Recognition Based on Global Texture Analysis
We describe a novel texture analysis-based approach toward font recognition. Existing methods are typically based on local typographical features that often require connected components analysis. In our method, we take the document as an image containing some specific textures and regard font recognition as texture identification. The method is content-independent and involves no detailed local feature analysis. Experiments are carried out by using 14000 samples of 24 frequently used Chinese fonts (six typefaces combined with four styles), as well as 32 frequently used English fonts (eight typefaces combined with four styles). An average recognition rate of 99.1 percent is achieved. Experimental results are also included on the robustness of the method against image degradation (e.g., pepper and salt noise) and on the comparison with existing methods.
17392876
High-level software energy macro-modeling
This paper presents an efficient and accurate high-level software energy estimation methodology using the concept of characterization-based macro-modeling. In characterization-based macro-modeling, a function or sub-routine is characterized using an accurate lower-level energy model of the target processor, to construct a macro-model that relates the energy consumed in the function under consideration to various parameters that can be easily observed or calculated from a high-level programming language description. The constructed macro-models eliminate the need for significantly slower instruction-level interpretation or hardware simulation that is required in conventional approaches to software energy estimation. We present two different approaches to macro-modeling for embedded software that offer distinct efficiency-accuracy characteristics: (i) complexity-based macro-modeling, where the variables that determine the algorithmic complexity of the function under consideration are used as macro-modeling parameters, and (ii) profiling-based macro-modeling, where internal profiling statistics for the functions are used as parameters in the energy macro-models. We have experimentally validated our software energy macro-modeling techniques on a wide range of embedded software routines and two different target processor architectures. Our experiments demonstrate that high-level macro-models constructed using the proposed techniques are able to estimate the energy consumption to within 95% accuracy on the average, while commanding speedups of one to five orders-of-magnitude over current instruction-level and architectural energy estimation techniques.
2598412
Embedded operating system energy analysis and macro-modeling
A large and increasing number of modern embedded systems are subject to tight power/energy constraints. It has been demonstrated that the operating system (OS) can have a significant impact on the energy efficiency of the embedded system. Hence, analysis of the energy effects of the OS is of great importance. Conventional approaches to energy analysis of the OS (and embedded software, in general) require the application software to be completely developed and integrated with the system software, and that either measurement on a hardware prototype or detailed simulation of the entire system be performed. Since this process requires significant design effort, unfortunately, it is typically too late in the design cycle to perform high-level or architectural optimizations on the embedded software, restricting the scope of power savings. Our work recognizes the need to provide embedded software designers with feedback about the effect of different OS services on energy consumption early in the design cycle. As a first step in that direction, this paper presents a systematic methodology to perform energy analysis and macro-modeling of an embedded OS. Our energy macro-models provide software architects and developers with an intuitive model for the OS energy effects, since they directly associate energy consumption with OS services and primitives that are visible to the application software. Our methodology consists of (i) an analysis stage, where we identify a set of energy components, called energy characteristics, which are useful to the designer in making OS-related design trade-offs, and (ii) a subsequent macromodeling stage, where we collect data for the identified energy components and automatically derive macro-models for them. We validate our methodology by deriving energy macro-models for two state-of-the-art embedded OS's, /spl mu/C/OS and Linux OS.
3247302
An Energy-Aware Synthesis Methodology for OS-Driven Multi-Process Embedded Software
The growing software content in various battery-driven embedded systems has led to significant interest in technologies for energy-efficient embedded software. While lowenergy software research has, in the past, focused on energy optimization at the instruction and source-code levels, approaches targeted at a higher software level are beginning to gain attention. In this work, we propose a methodology to refine a control-data flow diagram (CDFD) model of an embedded software program into an energy-efficient multi-process software architecture graph (SAG). Our starting representation, the CDFD, is capable of modeling data-dependent control flow. Energy efficiency is achieved by reducing the energy wastage due to context switches and inter-process communications (IPCs). Conditionally-unused computational operations, and their corresponding energy, are also avoided by a condition-aware static scheduler. Finally, code generation is performed from the energy-efficient SAG to produce a multi-process program that implements the original CDFD specification. Experimental results establish the efficacy of the proposed approach.