View All Items
(1 - 2 of 2)
- Title
- nanoengineered energy harvesting and storage devices.
- Creator
-
Li, Chao, Thomas, Jayan, Zhai, Lei, Yang, Yang, Gesquiere, Andre, Dong, Yajie, Sun, Wei, University of Central Florida
- Abstract / Description
-
Organic and perovskite solar cells have recently attracted significant attention due to itsflexibility, ease of fabrication and excellent performance. In order to realize even betterperformance for organic and perovskite solar cells, rejuvenated effort towards developingnanostructured electrodes and high quality active layer is necessary.In this dissertation, several strategic directions of enhancing the performance of organicand perovskite solar cells are investigated. An introduction and...
Show moreOrganic and perovskite solar cells have recently attracted significant attention due to itsflexibility, ease of fabrication and excellent performance. In order to realize even betterperformance for organic and perovskite solar cells, rejuvenated effort towards developingnanostructured electrodes and high quality active layer is necessary.In this dissertation, several strategic directions of enhancing the performance of organicand perovskite solar cells are investigated. An introduction and background of organic andperovskite solar cells, which includes motivation, classification and working principles,nanostructured electrode materials and solvent effect on active materials, and devices fabrication,are presented. A facile method, called Spin-on Nanoprinting (SNAP), to fabricate highly orderedZnO-AgNW-ZnO electrode is introduced to enhance the performance of organic solar cell.Subsequently, a ternary solvent method is developed to fabricate high Voc thieno[3,4-b]thiophene/benzodithiophene (PTB7) and indene-C60 bisadduct (ICBA)solar cells. Theperformance of the devices improved about 20% compared to those made by binary solventmethod. In order to understand the fundamental properties of the materials ruling theperformance of the PSCs tested, AFM-based nanoscale characterization techniques includingPulsed-Force-Mode AFM (PFM-AFM) and Mode-Synthesizing AFM (MSAFM) are introduced.These methods are used to study the morphology and physical properties of the structuresconstitutive of the active layers of the PSCs. Conductive-AFM (cAFM) studies reveal localvariations in conductivity in the donor and acceptor phases as well as an increase in photocurrentmeasured in the PTB7:ICBA sample obtained with the ternary solvent processing technique.Moreover, efficient perovskite solar cells with good transparency in the visible wavelength rangehave been developed by a facile and low-temperature PCBM-assisted perovskite growth method.This method results in the formation of perovskite-PCBM hybrid material at the grain boundaries which is observed by EELS mapping and confirmed by steady-state photoluminescence (PL)spectra and transient photocurrent (TP) measurements. This method involves fewer steps andtherefore is less expensive and time consuming than other reported methods. In addition, wereport an all solid state, energy harvesting and storing (ENHANS) filament which integratesperovskite solar cell (PSC) on top of a symmetric supercapacitor (SSC) via a copper filamentwhich works as a shared electrode for direct charge transfer. Developing ENHANS on a copperfilament provides a low-cost solution for flexible self-sufficient energy systems for wearablesand other portable devices. Finally, a summary of this dissertation as well as some potentialfuture directions are presented.
Show less - Date Issued
- 2016
- Identifier
- CFE0006693, ucf:51912
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0006693
- Title
- Exploring FPGA Implementation for Binarized Neural Network Inference.
- Creator
-
Yang, Li, Fan, Deliang, Zhang, Wei, Lin, Mingjie, University of Central Florida
- Abstract / Description
-
Deep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function,...
Show moreDeep convolutional neural network has taken an important role in machine learning algorithm. It is widely used in different areas such as computer vision, robotics, and biology. However, the models of deep neural networks become larger and more computation complexity which is a big obstacle for such huge model to implement on embedded systems. Recent works have shown the binarized neural networks (BNN), utilizing binarized (i.e. +1 and -1) convolution kernel and binarized activation function, can significantly reduce the parameter size and computation cost, which makes it hardware-friendly for Field-Programmable Gate Arrays (FPGAs) implementation with efficient energy cost. This thesis proposes to implement a new parallel convolutional binarized neural network (i.e. PC-BNN) on FPGA with accurate inference. The embedded PC-BNN is designed for image classification on CIFAR-10 dataset and explores the hardware architecture and optimization of customized CNN topology.The parallel-convolution binarized neural network has two parallel binarized convolution layers which replaces the original single binarized convolution layer. It achieves around 86% on CIFAR-10 dataset and owns 2.3Mb parameter size. We implement our PC-BNN inference into the Xilinx PYNQ Z1 FPGA board which only has 4.9Mb on-chip Block RAM. Since the ultra-small network parameter, the whole model parameters can be stored on on-chip memory which can greatly reduce energy consumption and computation latency. Meanwhile, we design a new pipeline streaming architecture for PC-BNN hardware inference which can further increase the performance. The experiment results show that our PC-BNN inference on FPGA achieves 930 frames per second and 387.5 FPS/Watt, which are among the best throughput and energy efficiency compared to most recent works.
Show less - Date Issued
- 2018
- Identifier
- CFE0007384, ucf:52067
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007384
