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- Genre: Masters Thesis
- Creators: Johnston, Carol
- Creators: Chakrabarti, Chaitali
- Creators: Cooke, Nancy J.
- Member of: ASU Electronic Theses and Dissertations
Description
Heterogenous SoCs are in development that marry multiple architectural patterns together. In order for software to be run on such a platform, it must be broken down into its constituent parts, kernels, and scheduled for execution on the hardware. Although this can be done by hand, it would be arduous and time consuming; rather, a tool should be developed that analyzes the source binary, extracts the kernels, schedules the kernels, and optimizes the scheduled kernels for their target component. This dissertation proposes a decidable kernel definition that enables an algorithmic approach to detecting kernels from arbitrary programs. This definition is built upon four constraints that can be tested using basic graph theory. In addition, two algorithms are proposed that successfully extract kernels based upon runtime information. The first utilizes dynamic traces, which are generated using a collection of novel optimizations. The second utilizes a simple affinity matrix, which has no runtime overhead during program execution. Finally, a Dense Neural Network is proposed that is capable of detecting a kernel's archetype based upon only the composition of the source program and the number of times individual basic blocks execute. The contributions proposed in this dissertation provide the necessary infrastructure to perform a litany of other optimizations on kernels. By detecting kernels algorithmically, any program can be analyzed and optimized with techniques that have heretofore required kernels be written in a compatible form. Computational kernels can be extracted from any program with no constraints. The innovations describes here will form the foundation for automated kernel optimization in the future, helping optimize the code of the future.
ContributorsUhrie, Richard Lawrence (Author) / Brunhaver, John (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Shrivastiva, Aviral (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2021
Description
Background: Smartphone diet tracking applications (apps) are increasing in popularity but may not adequately address the important concerns of proper intake and of diet quality. Two novel weight loss apps were designed based on the popular dietary frameworks: MyPlate and FoodLists. MyPlate, the dietary guidelines put forth by the U.S. government, encourages a balanced diet from five primary food groups, but does not specify intake limits. The Food Lists set upper intake limits on all food groups except vegetables, and these guidelines extend to include fats, sweets, and alcohol.
Objective: The purpose of this randomized controlled trial was to determine whether adherence to a weight loss app providing intake limits and more food group detail (the Food Lists app) facilitated more weight loss and better diet quality than adherence to a weight loss app based on the MyPlate platform. An additional objective was to examine whether higher app adherence would lead to greater weight loss.
Design: Thirty seven adults from a campus population were recruited, randomized, and instructed to follow either the Food Lists app (N=20) or the MyPlate app (N=17) for eight weeks. Subjects received one 15 minute session of diet and app training at baseline, and their use of the app was tracked daily. Body mass was measured at baseline and post-test.
Participants/setting: Healthy adults from a university campus population in downtown Phoenix, Arizona with BMI 24 to 40, medically stable, and who owned a smartphone.
Main outcome measures: Outcome measures included weight change, days of adherence, and diet quality change. Secondary measures included BMI, fat %, and waist circumference.
Statistical analysis: Descriptive statistics (means and standard errors); Repeated measures ANOVAs analyzing weight, diet quality, and BMI; Pearson and Spearman correlations analyzing adherence and weight loss.
Results: Repeated measures ANOVAs and correlations revealed no significant mean differences in primary outcome variables of weight loss, adherence, or diet quality (P=0.140; P=0.790; P=0.278). However, there was a significant mean reduction of BMI favoring the group using the Food Lists app (P=0.041).
Conclusion: The findings strengthen the idea that intake limits and food group detail may be associated with weight loss. Further investigation is warranted to determine whether longer use of the Food Lists app can produce more significant dieting successes and encourage healthier behavioral outcomes.
Objective: The purpose of this randomized controlled trial was to determine whether adherence to a weight loss app providing intake limits and more food group detail (the Food Lists app) facilitated more weight loss and better diet quality than adherence to a weight loss app based on the MyPlate platform. An additional objective was to examine whether higher app adherence would lead to greater weight loss.
Design: Thirty seven adults from a campus population were recruited, randomized, and instructed to follow either the Food Lists app (N=20) or the MyPlate app (N=17) for eight weeks. Subjects received one 15 minute session of diet and app training at baseline, and their use of the app was tracked daily. Body mass was measured at baseline and post-test.
Participants/setting: Healthy adults from a university campus population in downtown Phoenix, Arizona with BMI 24 to 40, medically stable, and who owned a smartphone.
Main outcome measures: Outcome measures included weight change, days of adherence, and diet quality change. Secondary measures included BMI, fat %, and waist circumference.
Statistical analysis: Descriptive statistics (means and standard errors); Repeated measures ANOVAs analyzing weight, diet quality, and BMI; Pearson and Spearman correlations analyzing adherence and weight loss.
Results: Repeated measures ANOVAs and correlations revealed no significant mean differences in primary outcome variables of weight loss, adherence, or diet quality (P=0.140; P=0.790; P=0.278). However, there was a significant mean reduction of BMI favoring the group using the Food Lists app (P=0.041).
Conclusion: The findings strengthen the idea that intake limits and food group detail may be associated with weight loss. Further investigation is warranted to determine whether longer use of the Food Lists app can produce more significant dieting successes and encourage healthier behavioral outcomes.
ContributorsScholtz, Cameron (Author) / Johnston, Carol (Thesis advisor) / Mayol-Kreiser, Sandra (Committee member) / Hekler, Eric (Committee member) / Arizona State University (Publisher)
Created2016
Description
Drinking vinegar is a popularly discussed remedy for relieving heartburn symptom, as can be read on many websites; however, there has been no scientific research or theory to support its efficacy. This randomized, placebo-controlled, double-blind, cross-over research study tested the efficacy of the organic apple cider vinegar, with mother, on alleviation of the heartburn symptom related to Gastro-esophageal reflux disease (GERD). A minimum of one week separated the four trial arms: chili (placebo), antacid after chili meal (positive control), vinegar added to chili, and diluted vinegar after chili meal. Twenty grams of vinegar were used in both vinegar treatments, and 10 grams of liquid antacid were used in the antacid trial. A five-point Likert scale and a 10-cm visual analogue scale (VAS) were used to assess heartburn severity during a 120 minutes testing time. Seven of 15 recruited subjects' data was usable for statistical analysis (age: 39.6 ± 12.2 y, body mass index (BMI): 29.4 ± 4.2 kg/m2, waist circumference: 36.4 ± 4.1 inch). There was no statistically significant difference among the mean and incremental area-under-the-curve (iAUC) heartburn scores among different trials (Likert scale questionnaire p= .259, VAS questionnaire p= .659, iAUC Likert scale p= .184, iAUC VAS p= .326). Seven participants were further divided into antacid responder (n=4) and antacid non-responder groups (n=3). Likert scale mean heartburn score and iAUC data in antacid responder group had significant finding (p= .034 and p= .017 respectively). The significance lay between antacid and 'vinegar added to chili' trials. Effect size was also used to interpret data due to the small sample size: Likert scale: mean heartburn score= .444, iAUC= .425; VAS mean heartburn score= .232, iAUC .611. Effect size for antacid responder group was Likert scale: mean heartburn score= .967, iAUC= .936. Future research is needed to examine whether ingesting organic vinegar benefits alleviation of heartburn symptom related to GERD for people who do not respond well to antacid.
ContributorsYeh, Zoe (Author) / Johnston, Carol (Thesis advisor) / Mayol-Kreiser, Sandra (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2016
Description
Articial Neural Network(ANN) has become a for-bearer in the field of Articial Intel-
ligence. The innovations in ANN has led to ground breaking technological advances
like self-driving vehicles,medical diagnosis,speech Processing,personal assistants and
many more. These were inspired by evolution and working of our brains. Similar
to how our brain evolved using a combination of epigenetics and live stimulus,ANN
require training to learn patterns.The training usually requires a lot of computation
and memory accesses. To realize these systems in real embedded hardware many
Energy/Power/Performance issues needs to be solved. The purpose of this research
is to focus on methods to study data movement requirement for generic Neural Net-
work along with the energy associated with it and suggest some ways to improve the
design.Many methods have suggested ways to optimize using mix of computation and
data movement solutions without affecting task accuracy. But these methods lack a
computation model to calculate the energy and depend on mere back of the envelope calculation. We realized that there is a need for a generic quantitative analysis
for memory access energy which helps in better architectural exploration. We show
that the present architectural tools are either incompatible or too slow and we need
a better analytical method to estimate data movement energy. We also propose a
simplistic yet effective approach that is robust and expandable by users to support
various systems.
ligence. The innovations in ANN has led to ground breaking technological advances
like self-driving vehicles,medical diagnosis,speech Processing,personal assistants and
many more. These were inspired by evolution and working of our brains. Similar
to how our brain evolved using a combination of epigenetics and live stimulus,ANN
require training to learn patterns.The training usually requires a lot of computation
and memory accesses. To realize these systems in real embedded hardware many
Energy/Power/Performance issues needs to be solved. The purpose of this research
is to focus on methods to study data movement requirement for generic Neural Net-
work along with the energy associated with it and suggest some ways to improve the
design.Many methods have suggested ways to optimize using mix of computation and
data movement solutions without affecting task accuracy. But these methods lack a
computation model to calculate the energy and depend on mere back of the envelope calculation. We realized that there is a need for a generic quantitative analysis
for memory access energy which helps in better architectural exploration. We show
that the present architectural tools are either incompatible or too slow and we need
a better analytical method to estimate data movement energy. We also propose a
simplistic yet effective approach that is robust and expandable by users to support
various systems.
ContributorsChowdary, Hidayatullah (Author) / Cao, Yu (Thesis advisor) / Seo, JaeSun (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2018