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Full-Text Articles in Engineering

Automated Program Profiling And Analysis For Managing Heterogeneous Memory Systems, Adam Palmer Howard Dec 2017

Automated Program Profiling And Analysis For Managing Heterogeneous Memory Systems, Adam Palmer Howard

Masters Theses

Many promising memory technologies, such as non-volatile, storage-class memories and high-bandwidth, on-chip RAMs, are beginning to emerge. Since each of these new technologies present tradeoffs distinct from conventional DRAMs, next-generation systems are likely to include multiple tiers of memory storage, each with their own type of devices. To efficiently utilize the available hardware, such systems will need to alter their data management strategies to consider the performance and capabilities provided by each tier.

This work explores a variety of cross-layer strategies for managing application data in heterogeneous memory systems. We propose different program profiling-based techniques to automatically partition program allocation …


Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu Aug 2017

Wide-Area Measurement-Driven Approaches For Power System Modeling And Analytics, Hesen Liu

Doctoral Dissertations

This dissertation presents wide-area measurement-driven approaches for power system modeling and analytics. Accurate power system dynamic models are the very basis of power system analysis, control, and operation. Meanwhile, phasor measurement data provide first-hand knowledge of power system dynamic behaviors. The idea of building out innovative applications with synchrophasor data is promising.

Taking advantage of the real-time wide-area measurements, one of phasor measurements’ novel applications is to develop a synchrophasor-based auto-regressive with exogenous inputs (ARX) model that can be updated online to estimate or predict system dynamic responses.

Furthermore, since auto-regressive models are in a big family, the ARX model …


Tiled Danna: Dynamic Adaptive Neural Network Array Scaled Across Multiple Chips, Patricia Jean Eckhart Aug 2017

Tiled Danna: Dynamic Adaptive Neural Network Array Scaled Across Multiple Chips, Patricia Jean Eckhart

Masters Theses

Tiled Dynamic Adaptive Neural Network Array(Tiled DANNA) is a recurrent spiking neural network structure composed of programmable biologically inspired neurons and synapses that scales across multiple FPGA chips. Fire events that occur on and within DANNA initiate spiking behaviors in the programmable elements allowing DANNA to hold memory through the synaptic charge propagation and neuronal charge accumulation. DANNA is a fully digital neuromorphic computing structure based on the NIDA architecture. To support initial prototyping and testing of the Tiled DANNA, multiple Xilinx Virtex 7 690Ts were leveraged. The primary goal of Tiled DANNA is to support scaling of DANNA neural …


Scalable High-Speed Communications For Neuromorphic Systems, Aaron Reed Young Aug 2017

Scalable High-Speed Communications For Neuromorphic Systems, Aaron Reed Young

Masters Theses

Field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), and other chip/multi-chip level implementations can be used to implement Dynamic Adaptive Neural Network Arrays (DANNA). In some applications, DANNA interfaces with a traditional computing system to provide neural network configuration information, provide network input, process network outputs, and monitor the state of the network. The present host-to-DANNA network communication setup uses a Cypress USB 3.0 peripheral controller (FX3) to enable host-to-array communication over USB 3.0. This communications setup has to run commands in batches and does not have enough bandwidth to meet the maximum throughput requirements of the DANNA device, resulting …


Optimization Of Spatial Convolution In Convnets On Intel Knl, Sangamesh Nagashattappa Ragate May 2017

Optimization Of Spatial Convolution In Convnets On Intel Knl, Sangamesh Nagashattappa Ragate

Masters Theses

Most of the experts admit that the true behavior of the neural network is hard to predict. It is quite impossible to deterministically prove the working of the neural network as the architecture gets bigger, yet, it is observed that it is possible to apply a well engineered network to solve one of the most abstract problems like image recognition with substantial accuracy. It requires enormous amount of training of a considerably big and complex neural network to understand its behavior and iteratively improve its accuracy in solving a certain problem. Deep Neural Networks, which are fairly popular nowadays deal …