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The aim of this paper is to propose a transformation algorithm for multi-granularity linguistic information assessed in different unbalanced linguistic term sets together with its application in linguistic group decision making (LGDM) problem. Assuming that the linguistic information given to the alternatives by different decision makers distribute in different granularity and/or semantic term sets...
Natural Steganography (NS) uses the concept of cover-source switching to provide good undetectability performances [1]. The sensor noise of the source (camera) for a given ISO sensitivity ISO1 is first modeled as an independent Gaussian distribution for each photo-site, then the embedding mimics a switch to another sensitivity ISO2(> ISO1). Because the embedding has to be performed on developed...
In content-based image retrieval systems, visual content of the image is the criterion for measuring image similarity. We propose a method to solve the problem of loss of spatial information of objects when local descriptors from an image with multiple objects are aggregated to form a global representation. In our approach, after saliency-based spatial partitioning, local feature descriptors from...
Compressed domain human action recognition algorithms are extremely efficient, because they only require a partial decoding of the video bit stream. However, the question what exactly makes these algorithms decide for a particular action is still a mystery. In this paper, we present a general method, Layer-wise Relevance Propagation (LRP), to understand and interpret action recognition algorithms...
Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses...
We propose a methodology to determine the suitability of different data representations in terms of their error-tolerance for a given application with accelerator-based computing. This methodology helps match the characteristics of a representation to the data access patterns in an application. For this, we first identify a benchmark of key kernels from linear algebra that can be used to construct...
This paper presents Bayesian Representation-based Classification (BRC), an approach based on sparse Bayesian regression and subspace clustering for image set classification. Similar to existing representation-based approaches such as Sparse RC (SRC) and Collaborative RC (CRC), BRC assumes that a test image is approximated by a linear combination of the gallery images of the true class. However, we...
Exascale computing is facing a gap between the ever increasing demand for application performance and the underlying chip technology that does no longer deliver the expected exponential increases in CPU performance. The industry is now progressively moving towards dedicated accelerators to deliver high performance and better energy efficiency. However, the question of programmability still remains...
As throughput-oriented accelerators, GPUs provide tremendous processing power by running a massive number of threads in parallel. However, exploiting high degrees of thread-level parallelism (TLP) does not always translate to the peak performance that GPUs can offer, leaving the GPU's resources often under-utilized. Compared to compute resources, memory resources can tolerate considerably lower levels...
This paper shows that many applications exhibit execution-phase-specific sensitivity towards approximation of the internal subcomputations. Therefore, approximation in certain phases can be more beneficial than others. Further, this paper presents Opprox, a novel system for application's execution-phase-aware approximation. For a user provided error budget and target input parameters, Opprox identifies...
The recent adoption of OpenCL programming model by FPGA vendors has realized the function portability of OpenCL workloads on FPGA. However, the poor performance portability prevents its wide adoption. To harness the power of FPGAs using OpenCL programming model, it is advantageous to design an analytical performance model to estimate the performance of OpenCL workloads on FPGAs and provide insights...
Iterative stencil algorithms find applications in a wide range of domains. FPGAs have long been adopted for computation acceleration due to its advantages of dedicated hardware design. Hence, FPGAs are a compelling alternative for executing iterative stencil algorithms. However, efficient implementation of iterative stencil algorithms on FPGAs is very challenging due to the data dependencies between...
Emerging applications require computing platforms to extract task-relevant information from increasingly large amounts of data. These requirements place stringent constraints on energy efficiency, throughput, latency, and for certain data types, security and privacy of computing platforms. Traditionally, silicon CMOS scaling has been relied upon to meet these energy and delay constraints. However,...
Recent research studies have shown that modern GPU performance is often limited by the memory system performance. Optimizing memory hierarchy performance requires GPU designers to draw design insights based on the cache & memory behavior of end-user applications. Unfortunately, it is often difficult to get access to end-user workloads due to the confidential or proprietary nature of the software/data...
Convolution is a fundamental operation in many applications, such as computer vision, natural language processing, image processing, etc. Recent successes of convolutional neural networks in various deep learning applications put even higher demand on fast convolution. The high computation throughput and memory bandwidth of graphics processing units (GPUs) make GPUs a natural choice for accelerating...
REDEFINE is a distributed dynamic dataow architecture, designed for exploiting parallelism at various granularities as an embedded system-on-chip (SoC). is paper dwells on the exibility of REDEFINE architecture and its execution model in accelerating real-time applications coupled with a WCET analyzer that computes execution time bounds of real time applications.
This paper introduces a method of decoupling affine computations-a class of expressions that produces extremely regular values across SIMT threads-from the main execution stream, so that the affine computations can be performed with greater efficiency and with greater independence from the main execution stream. This decoupling has two benefits: (1) For compute-bound programs, it significantly reduces...
With the end of Dennard scaling, architects have increasingly turned to special-purpose hardware accelerators to improve the performance and energy efficiency for some applications. Unfortunately, accelerators don't always live up to their expectations and may under-perform in some situations. Understanding the factors which effect the performance of an accelerator is crucial for both architects and...
Extreme machine learning and its variants have shown good generalization performance and high leaning speed in many applications through its fast convergence. Despite the parallel and distributed ELM on MapReduce framework able to handle very large scale dataset for bigdata applications, the process of coping up with the rapidly updating data is a challenging one. Among the unified algorithms, the...
This paper reports the identification of nonlinear models for wireless communications systems. The procedure relies on a novel complex-valued Volterra series (CVS) representation to provide a sparse representation based on statistical hypothesis testing and the Bayesian information criterion (BIC). The approach has been experimentally evaluated with the front-end of a communications transmitter taking...
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