Open Access. Powered by Scholars. Published by Universities.®
![Digital Commons Network](http://assets.bepress.com/20200205/img/dcn/DCsunburst.png)
Computer and Systems Architecture Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Anomaly (1)
- Autoencoders (1)
- Data Reduction (1)
- Deep Learning (1)
- Design space search (1)
-
- Dilution (1)
- Edge Computing (1)
- Field programmable gate array (1)
- HARP (1)
- Heterogeneous architecture (1)
- High-level synthesis (1)
- Human Activity Recognition (1)
- Human-Information Interaction (1)
- IoT (1)
- Levenberg (1)
- Marquardt (1)
- Metric (1)
- OpenCL (1)
- Real-Time Twitter Analysis (1)
- SGEMM (1)
- Stream Processing (1)
- TDOA (1)
- Visual Analytics (1)
Articles 1 - 4 of 4
Full-Text Articles in Computer and Systems Architecture
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 …
Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh
Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh
Electronic Thesis and Dissertation Repository
Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …
Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann
Nonlinear Least Squares 3-D Geolocation Solutions Using Time Differences Of Arrival, Michael V. Bredemann
Mathematics & Statistics ETDs
This thesis uses a geometric approach to derive and solve nonlinear least squares minimization problems to geolocate a signal source in three dimensions using time differences of arrival at multiple sensor locations. There is no restriction on the maximum number of sensors used. Residual errors reach the numerical limits of machine precision. Symmetric sensor orientations are found that prevent closed form solutions of source locations lying within the null space. Maximum uncertainties in relative sensor positions and time difference of arrivals, required to locate a source within a maximum specified error, are found from these results. Examples illustrate potential requirements …
A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki
A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki
Electronic Thesis and Dissertation Repository
Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data interaction, and …