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The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose...
From self-driving cars to high dynamic range (HDR) imaging, the demand for image-based applications is growing quickly. In mobile systems, these applications place particular strain on performance and energy efficiency. As traditional memory systems are optimized for 1D memory access, they are unable to efficiently exploit the multi-dimensional locality characteristics of image-based applications...
Superpixel generation is a common preprocessing step in vision processing aimed at dividing an image into non-overlapping regions. Simple Linear Iterative Clustering (SLIC) is a commonly used superpixel algorithm that offers a good balance between performance and accuracy. However, the algorithm's high computational and memory bandwidth requirements result in performance and energy efficiency that...
The capabilities of mobile devices have been increasing at a momentous rate. As better processors have merged with capable cameras in mobile systems, the number of computer vision applications has grown rapidly. However, the computational and energy constraints of mobile devices have forced computer vision application developers to sacrifice accuracy for the sake of meeting timing demands. To increase...
Visual Sonification is the process of converting visual properties of objects into sound signals. This paper describes the Michigan Visual Sonification System (MVSS) that utilizes this process to assist the visually impaired in distinguishing different objects in their surroundings. MVSS uses depth information to first segment and localize salient objects and then represents an object's appearance...
The growth in mobile vision applications, coupled with the performance limitations of mobile platforms, has led to a growing need to understand computer vision applications. Computationally intensive mobile vision applications, such as augmented reality or object recognition, place significant performance and power demands on existing embedded platforms, often leading to degraded application quality...
The deployment of computer vision algorithms in mobile applications is growing at a rapid pace. A primary component of the computer vision software pipeline is feature extraction, which identifies and encodes relevant image features. We present an embedded heterogeneous multicore design named EFFEX that incorporates novel functional units and memory architecture support, making it capable of increasing...
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