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Modern machine-learning techniques greatly reduce the efforts required to conduct high-quality program compilation, which, without the aid of machine learning, would otherwise heavily rely on human manipulation as well as expert intervention. The success of the application of machine-learning techniques to compilation tasks can be largely attributed to the recent development and advancement of program...
Stochastic computing (SC) performs arithmetic on randomized bit-streams called stochastic numbers (SNs) using standard logic circuits. SC has many appealing features such as error tolerance, low power, and low area cost. However, it suffers from severe accuracy loss due to correlation or insufficient randomness. SNs can be decorrelated by regenerating them from independent random sources. This is...
Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g...
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social networks, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile...
Stochastic computing (SC) is a re-emerging technique to process probability data encoded in digital bit-streams. Its main advantage is that arithmetic operations can be implemented by extremely small and low-power logic circuits. This makes SC suitable for signal-processing applications involving matrix operations whose VLSI implementation is very costly. Previous SC approaches only address basic...
Thread divergence frequently occurs in various GPGPU applications while parallel threads encounter branching, especially for multimedia algorithms involving high-level image processing and computer vision algorithms. Either predicate or branch instruction degrades the parallel processing performance as a result of synchronization cost. In this work, we propose a configurable thread compaction unit...
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