Browsing by Subject "Linear interpolation"
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Item RTL design and analysis of Softmax Layer in Deep Neural Networks(2020-05-07) Xavier, Jim; John, Lizy KurianDeep neural networks (DNNs) are widely used in modern machine learning systems in the big data era for their superior accuracy. These artificial neural networks suffer from high computational complexity. The structure of DNN layers vary depending on the nature of training and inference tasks. Softmax Layer is a critical layer in DNNs and is usually used as the output layer in multi-category classification tasks. Softmax layer involves exponentiation and division, thereby resulting in high computational complexity and long critical paths. This report focuses on frontend implementation of an efficient microarchitecture of Softmax layer, which tries to address some of the problems associated with a simple, direct implementation. Techniques like pipelining are employed to boost the performance of the complex datapath logic. Error analysis of the hardware is performed with software results from MATLAB. Synthesis of the RTL code is performed on Xilinx Artix-7 FPGA, resulting in a clock frequency of 274.3 MHz.Item Using machine learning techniques to simplify mobile interfaces(2012-12) Sigman, Matthew Stephen; Julien, Christine, D. Sc.; Ghosh, JoydeepThis paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation.