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Visual data comprise of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional dataset is transformed into a hierarchy of signals to expose its multi-scale structures. The(More)
The Brain-State-in-a-Box (BSB) model is an auto-associative neural network that has been widely used in optical character recognition and image processing. Traditionally, the BSB model was realized at software level and carried out on high-performance computing clusters. To improve computation efficiency and reduce resources requirement, we propose a(More)
In this paper we present a methodology and techniques for generating cycle-accurate macro-models for RT-level power analysis. The proposed macro-model predicts not only the cycle-by-cycle power consumption of a module, but the power profile of the module over time. The proposed methodology consists of three steps: module equation form generation and(More)
<italic>In this paper, we introduce a new technique for modeling and solving the dynamic power management (DPM) problem for systems with complex behavioral characteristics such as concurrency, synchronization, mutual exclusion and conflict. We model a power-managed distributed computing system as a controllable Generalized Stochastic Petri Net (GSPN) with(More)
In this paper, an energy aware dynamic voltage and frequency selection (EA-DVFS) algorithm is proposed. The EA-DVFS algorithm adjusts the processor's behavior depending on the summation of the stored energy and the harvested energy in a future duration. Specifically, if the system has sufficient energy, tasks are executed at full speed; otherwise, the(More)
In this paper, we propose a statistical power evaluation framework at the RT-level. We first discuss the power macro-modeling formulation, and then propose a simple random sampling technique to alleviate the the overhead of macro-modeling during RTL simulation. Next, we describe a regression estimator to reduce the error of the macro-modeling approach.(More)