Efficient Hardware Implementation of Deep Learning Networks Based on the Convolutional Neural Network

Image classification, speech processing, autonomous driving, and medical diagnosis have made the adoption of Deep Neural Networks (DNN) mainstream. Many deep networks such as AlexNet, GoogleNet, ResidualNet, MobileNet, YOLOv3 and Transformers have achieved immense success and popularity. However, implementing these deep and complex networks in hardware is a challenging feat. The growing demand of DNN applications in mobile devices and data centers have led the researchers to explore application specific hardware accelerators for DNNs. There have been numerous hardware and software based solutions to improve DNN throughput, latency, performance and accuracy. Any solution for hardware acceleration needs to optimize in a space confined by these metrics. Hardware acceleration of Deep Neural Networks (DNN) is a highly effective and viable solution for running them on mobile devices. The power of DNN is now available at the edge in a compact and power-efficient form factor because of hardware acceleration. In this thesis, we introduce a novel architecture that uses a generalized method called Single Input Partial Product 2-Dimensional Convolution (SIPP2D Convolution) which calculates a 2-D convolution in a fast and expedient manner. We present the exploration designs that have culminated into SIPP2D and emphasize its benefits. SIPP2D architecture prevents the re-fetching of input weights for the calculation of partial products. It can calculate the output of any input size and kernel size with a low memory-traffic while maintaining a low latency and high throughput compared to other popular techniques. In addition to being compatible with any input and kernel size, SIPP2D architecture can be modified to support any allowable stride. We describe the data flow and algorithmic modifications to SIPP2D which extends its capabilities to accommodate multi-stride convolutions. Supporting multi-stride convolutions is an essential feature addition to SIPP2D architecture, increasing its versatility and network agnostic character for convolutional type DNNs. Along with architectural explorations, we have also performed research in the area of model optimization. It is widely understood that any change on the algorithmic level of the network pays significant dividends at the hardware level. Compression and optimization techniques such as pruning and quantization help reduce the size of the model while maintaining the accuracy at an acceptable level. Thus, by combining techniques such as channel pruning with SIPP2D we can only boost its performance. In this thesis, we examine the performance of channel pruned SIPP2D compared to other compressed models. Traditionally, quantization of weights and inputs are used to reduce the memory transfer and power consumption. However, quantizing the outputs of layers can be a challenge since the output of each layer changes with the input. In our research, we use quantization on the output of each layer for AlexNet and VGGNet-16 to analyze the effect it has on accuracy. We use Signal to Noise Quantization Ratio (SQNR) to empirically determine the integer length (IL) as well as the fractional length (FL) for the fixed point precision that can yields the lowest SQNR and highest accuracy. Based on our observations, we can report that accuracy is sensitive to fractional length as well as integer length. For AlexNet, we observe deterioration in accuracy as the word length decreases. The Top -5 accuracy goes from 77% for floating point precision to 56% for a WL of 12 and FL of 8. The results are similar in the case of VGGNet-16. The Top-5 accuracy for VGGNet-16 decreases from 82% for floating point to 30% for a WL of 12 and FL of 8. In addition to the small word length, we observe the accuracy to be highly dependent on the integer length as well as the fractional length. We have also done analysis on the loss after retraining post quantization. We use polynomial fitting to achieve a relationship with fractional length and the drop in accuracy still sustained after retraining a quantized network. In summary, the winning combination of the enhanced SIPP2D architecture and compression techniques such as channel pruning and quantization techniques is highly advantageous and conducive to widespread adoption. SIPP2D architecture, with its flexible data flow and algorithmic modifications to support multi-stride convolutions, offers a powerful and versatile framework for deep neural networks

Stationary Automata

In this dissertation, we investigate new automata, we call it stationary automata or ST-automata. This concept is based on the definition of TF-automaton by Wojciechowski [Woj2]. What is new in our approach is that we incorporate stationary subsets of limit ordinals of uncountable cofinality. The first objective of the thesis is to motivate the new construction of automata. This concept of ST-automata allows us to make a connection with infinite graph theory. Aharoni, Nash-Williams, and Shelah [AhNaSh] formulated a condition that is necessary and sufficient for a bipartite graph to have a matching. For a bipartite graph G=( M,W,E ) , we define a language over the alphabet { M,W } . We construct an ST-automaton such that for each bipartite graph G, the automaton accepts an element of if and only if G has no matching. The theorem of Aharoni, Nash-Williams, and Shelah [AhNaSh] is used to prove that has the above property. The second objective is to compare the new ST-automata to TF-automata defined by Wojciechowski [Woj2]. First, adding an extra condition, we define special ST-automata and prove that they are equivalent to TF-automata. Then we show that in general ST-automata are stronger. We give an example of a language accepted by an ST-automaton that is not accepted by any special ST-automaton. In chapter four, we define operations on ST-automata over a fixed alphabet I as union, intersection, concatenation, raising to the powers, ω , *, and #. We show that applying those operations to languages defined by ST-automata the obtained languages are also definable using ST-automata

Analysis Of Green Financial Markets

Green financial markets are still in early stages of development. There are new green venture capital funds and green stocks to satisfy needs of project developers and investors. Various governments are also subsidizing new green ventures and project developers should take full advantage of these incentives. Also, there have been attempts to quantify riskiness of various green financial products so investors can make informed decisions

Assessment of Rotavirus Vaccine Type and Number of Doses on Severity of Disease

Background: Rotavirus disease is the leading global cause of severe diarrhea in children under 5 years. We examined the association between different rotavirus vaccines doses and severity of diarrhea. Methods: A secondary analysis of surveillance of children with acute gastroenteritis (AGE) symptoms during two seasons (January-June) in 2010 and 2011 from three pediatric hospitals in Atlanta, Georgia was conducted. Enrolled children were tested for rotavirus, using EIA (Rotaclone) and vaccination records were collected from the state immunization registry and healthcare providers. Cases were defined as any enrolled child who tested positive for rotavirus. Each enrolled child was assigned a Vesikari score to assess AGE severity. Results: 63.9% of participants had severe AGE. Cases were more likely to have severe AGE than controls (OR 3.8, 95% CI: 2.2-6.5). Receiving a mixed vaccine regimen had similar protection against severe disease to receiving only RotaTeq® or Rotarix® (Mixed: OR 0.1, 95% CI: 0.02-0.5; RotaTeq®: OR 0.1, 95% CI: 0.02-0.5; Rotarix®: OR 0.1; 95% CI 0.01-0.3). When controlling for vaccine type and demographic covariates, three doses of vaccine offered significant protection against severe disease (OR 0.3, 95% CI: 0.2-0.6). Conclusions: Receiving a mixed regimen of rotavirus vaccine is effective in preventing severe AGE. Mixed rotavirus vaccine regimens were equally efficacious to receiving a single type of vaccine in preventing severe disease. Three doses of vaccine, regardless of type, were effective in preventing severe disease but one or two doses were not

Leadership Of Global Carbon Market

Carbon credits trading and the need to reduce carbon emissions are slowly being accepted as new realities of global business. A number of institutions played crucial role in developing the global carbon market. The World Bank Group, the World Resource Institute, the Institutional Investor’s Group on Climate Change, and the Carbon Disclosure Project are some of the pioneers in developing the global carbon industry. The United States government did not assume the leadership role of the global carbon market as expected by world governments and corporations. The purpose of this study is to analyze the United States government’s position vis-à-vis the global carbon market and explore the leadership role played by other global institutions

Determinants Of Success In International Involvement Of Large U.S. Corporations

The findings of this study help to identify factors explaining success in international operations of large U.S. corporations. Successful foreign direct investment (FDI) and continuous involvement of large U.S. corporations is crucial for economic development of a country and social uplifting of many citizens of the world. Data were collected on selected U.S. corporations’ perception of the success variables in their international operation. Results indicate that large market size, geographical diversification, and low production/operating cost are the three most important success determinants of international operations

Method Development For The Detection Of Dichlorodiphenyltrichloroethane (Ddt) Metabolites In Meconium Using Gas Chromatography-Mass Spectrometry

DDT ialah sejenis racun perosak organoklorin yang telah digunakan secara meluas di seluruh dunia untuk tujuan pertanian dan kawalan malaria sekitar tahun 1950an dan 1960an. DDT is an organochlorine pesticide that was used heavily worldwide in the 1950s and 1960s both in agricultural production and for malaria control. It was discovered by a Swiss Scientist Dr. Paul Muller in 1942

Method Development For The Detection Of Dichlorodiphenyltrichloroethane (DDT) Metabolites In Meconium Using Gas Chromatography-Mass Spectrometry [SB950.A3 A532 2007 f rb].

DDT ialah sejenis racun perosak organoklorin yang telah digunakan secara meluas di seluruh dunia untuk tujuan pertanian dan kawalan malaria sekitar tahun 1950an dan 1960an. DDT is an organochlorine pesticide that was used heavily worldwide in the 1950s and 1960s both in agricultural production and for malaria control

Fixed Point Theorems and Iterative Function System in G-Metric Spaces

في هذا البحث قدمنا مؤثر هجسن_برنسلي (بأختصار,مؤثر H-B) على فضاءات G المترية وتوظيف الجانب النظري لأنشاء مجموعة كسورية كنقطة صامدة وحيدة له وذلك باستخدام نمط من تطبقات سيرك   Fالانكماشية المعممة المعرفة على  فضاءات  G المترية الكاملة. لقد تم توضيح بعض المفاهيم بأمثلة عددية.Iterated function space is a method to construct fractals and the results are self-similar. In this paper, we introduce the Hutchinson Barnsley operator (shortly, operator) on a  metric space and employ its theory to construct a fractal set as its unique fixed point by using Ciric type generalized -contraction in complete metric space. In addition, some concepts are illustrated by numerical examples