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We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing...
Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved performance of additional layers in a deep network comes at the cost of added latency and energy usage in feedforward inference. As networks continue to get deeper and larger, these costs become more prohibitive for real-time...
Integrated circuit manufacturing consists of tests at various stages to ensure functionality and performance using numerous test metrics for each system on chip (SoC) captured as part of assessment. At a later stage, functional units are evaluated in terms of multiple performance characteristics. In this paper, we propose a system that uses test metrics as features for machine learning models to predict...
We present Sparse Coding trees (SC-trees), a sparse coding-based framework for resolving misclassifications arising when multiple classes map to a common set of features. SC-trees are novel supervised classification trees that use node-specific dictionaries and classifiers to direct input based on classification results in the feature space at each node. We have applied SC-trees to emotion classification...
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