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Full-Text Articles in Computer Engineering
Domain Specific Computing In Tightly-Coupled Heterogeneous Systems, Anthony Michael Cabrera
Domain Specific Computing In Tightly-Coupled Heterogeneous Systems, Anthony Michael Cabrera
McKelvey School of Engineering Theses & Dissertations
Over the past several decades, researchers and programmers across many disciplines have relied on Moores law and Dennard scaling for increases in compute capability in modern processors. However, recent data suggest that the number of transistors per square inch on integrated circuits is losing pace with Moores laws projection due to the breakdown of Dennard scaling at smaller semiconductor process nodes. This has signaled the beginning of a new “golden age in computer architecture” in which the paradigm will be shifted from improving traditional processor performance for general tasks to architecting hardware that executes a class of applications in a …
Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris
Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris
McKelvey School of Engineering Theses & Dissertations
The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …
Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang
Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang
McKelvey School of Engineering Theses & Dissertations
Artificial neural networks(ANNs) are recognized as high-performance models for classification problems. They have proved to be efficient tools for many of today's applications like automatic driving, image and video recognition and restoration, big-data analysis. However, high performance deep neural networks have millions of parameters, and the iterative training procedure thus involves a very high computational cost. This research attempts to study the relationships between parameters in convolutional neural networks(CNNs). I assume there exists a certain relation between adjacent convolutional layers and proposed a machine learning model(MLM) that can be trained to represent this relation. The MLM's generalization ability is evaluated …