8559676
A simulation framework for energy-consumption analysis of OS-driven embedded applications
Energy consumption has become a major focus in the design of embedded systems (e.g., mobile computing and wireless communication devices). In particular, a shift of emphasis from hardware-oriented low-energy design techniques to energy-efficient embedded software design has occurred progressively in the past few years. To that end, various techniques have been developed for the design of energy-efficient embedded software. In operating system (OS)-driven embedded systems, the OS has a significant impact on the system's energy consumption directly (energy consumption associated with the execution of the OS functions and services), as well as indirectly (interaction of the OS with the application software). As a first step toward designing energy-efficient OS-based embedded systems, it is important to analyze the energy consumption of embedded software by taking the OS energy characteristics into account. To facilitate such studies, we present, in this work, an energy simulation framework that can be used to analyze the energy consumption characteristics of an embedded system featuring the embedded Linux OS running on the StrongARM processor. The framework allows software designers to study the energy consumption of the system software in relation to the application software, identify the energy hot spots, and perform design changes based on the knowledge of the OS energy consumption characteristics as well as application-OS interactions.
625121
A comprehensive high-level synthesis system for control-flow intensive behaviors
In this paper, we describe a comprehensive high-level synthesis system for control-flow intensive as well as data-dominated behaviors. We propose a new control-data flow graph model to preserve the parallelism inherent in the application, as well as to facilitate high-level synthesis. Our algorithm, which is based on an iterative improvement strategy, performs clock selection, scheduling, module selection, resource allocation and assignment simultaneously to fully derive the benefits of design space exploration at the behavior level. The system can be used to optimize area, power or energy, by selecting the cost function accordingly. Experimental results show that for energy-optimized designs, energy is reduced by up to 79.4% (an average of 42.2%), with an average of 24.8% area overhead, compared to area-optimized designs. For power-optimized designs, power is reduced by up to 70.8% (an average of 56.7%), with an average of 25.2% area overhead, compared to area-optimized designs. No Vdd scaling is performed to obtain the above results.
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.
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.
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.
11321791
A new face detection method based on shape information
Automatic detection of human faces is one of the most difficult problems in pattern recognition. In many practical applications (e.g. personal identification using photo-IDs), face images often have a simple background. In this paper, we propose a new method based on shape information. Our system can be very efficient for images with simple background. Input image is first enhanced by means of histogram equalization, followed by edge detection based on a multiple-scale filter. The extracted edges are then linked using a method based on an energy function. The face contour is finally extracted using the direction information of the linked edges. Experimental results are included to verify the effectiveness of this method. (C) 2000 Elsevier Science B.V. All rights reserved.
37912790
Asia Pacific Bioinformatics Network
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.