501 research outputs found
Development of a Parallel BAT and Its Applications in Binary-state Network Reliability Problems
Various networks are broadly and deeply applied in real-life applications.
Reliability is the most important index for measuring the performance of all
network types. Among the various algorithms, only implicit enumeration
algorithms, such as depth-first-search, breadth-search-first, universal
generating function methodology, binary-decision diagram, and
binary-addition-tree algorithm (BAT), can be used to calculate the exact
network reliability. However, implicit enumeration algorithms can only be used
to solve small-scale network reliability problems. The BAT was recently
proposed as a simple, fast, easy-to-code, and flexible make-to-fit
exact-solution algorithm. Based on the experimental results, the BAT and its
variants outperformed other implicit enumeration algorithms. Hence, to overcome
the above-mentioned obstacle as a result of the size problem, a new parallel
BAT (PBAT) was proposed to improve the BAT based on compute multithread
architecture to calculate the binary-state network reliability problem, which
is fundamental for all types of network reliability problems. From the analysis
of the time complexity and experiments conducted on 20 benchmarks of
binary-state network reliability problems, PBAT was able to efficiently solve
medium-scale network reliability problems
Distributed deep learning inference in fog networks
Today's smart devices are equipped with powerful integrated chips and built-in heterogeneous sensors that can leverage their potential to execute heavy computation and produce a large amount of sensor data. For instance, modern smart cameras integrate artificial intelligence to capture images that detect any objects in the scene and change parameters, such as contrast and color based on environmental conditions. The accuracy of the object recognition and classification achieved by intelligent applications has improved due to recent advancements in artificial intelligence (AI) and machine learning (ML), particularly, deep neural networks (DNNs).
Despite the capability to carry out some AI/ML computation, smart devices have limited battery power and computing resources. Therefore, DNN computation is generally offloaded to powerful computing nodes such as cloud servers. However, it is challenging to satisfy latency, reliability, and bandwidth constraints in cloud-based AI. Thus, in recent years, AI services and tasks have been pushed closer to the end-users by taking advantage of the fog computing paradigm to meet these requirements. Generally, the trained DNN models are offloaded to the fog devices for DNN inference. This is accomplished by partitioning the DNN and distributing the computation in fog networks.
This thesis addresses offloading DNN inference by dividing and distributing a pre-trained network onto heterogeneous embedded devices. Specifically, it implements the adaptive partitioning and offloading algorithm based on matching theory proposed in an article, titled "Distributed inference acceleration with adaptive dnn partitioning and offloading". The implementation was evaluated in a fog testbed, including Nvidia Jetson nano devices. The obtained results show that the adaptive solution outperforms other schemes (Random and Greedy) with respect to computation time and communication latency
Doctor of Philosophy
dissertationThe design of integrated circuit (IC) requires an exhaustive verification and a thorough test mechanism to ensure the functionality and robustness of the circuit. This dissertation employs the theory of relative timing that has the advantage of enabling designers to create designs that have significant power and performance over traditional clocked designs. Research has been carried out to enable the relative timing approach to be supported by commercial electronic design automation (EDA) tools. This allows asynchronous and sequential designs to be designed using commercial cad tools. However, two very significant holes in the flow exist: the lack of support for timing verification and manufacturing test. Relative timing (RT) utilizes circuit delay to enforce and measure event sequencing on circuit design. Asynchronous circuits can optimize power-performance product by adjusting the circuit timing. A thorough analysis on the timing characteristic of each and every timing path is required to ensure the robustness and correctness of RT designs. All timing paths have to conform to the circuit timing constraints. This dissertation addresses back-end design robustness by validating full cyclical path timing verification with static timing analysis and implementing design for testability (DFT). Circuit reliability and correctness are necessary aspects for the technology to become commercially ready. In this study, scan-chain, a commercial DFT implementation, is applied to burst-mode RT designs. In addition, a novel testing approach is developed along with scan-chain to over achieve 90% fault coverage on two fault models: stuck-at fault model and delay fault model. This work evaluates the cost of DFT and its coverage trade-off then determines the best implementation. Designs such as a 64-point fast Fourier transform (FFT) design, an I2C design, and a mixed-signal design are built to demonstrate power, area, performance advantages of the relative timing methodology and are used as a platform for developing the backend robustness. Results are verified by performing post-silicon timing validation and test. This work strengthens overall relative timed circuit flow, reliability, and testability
Shallow2Deep: Restraining Neural Networks Opacity through Neural Architecture Search
Recently, the Deep Learning (DL) research community has focused on developing efficient and highly performing Neural Networks (NN). Meanwhile, the eXplainable AI (XAI) research community has focused on making Machine Learning (ML) and Deep Learning methods interpretable and transparent, seeking explainability. This work is a preliminary study on the applicability of Neural Architecture Search (NAS) (a sub-field of DL looking for automatic design of NN structures) in XAI. We propose Shallow2Deep, an evolutionary NAS algorithm that exploits local variability to restrain opacity of DL-systems through NN architectures simplification. Shallow2Deep effectively reduces NN complexity – therefore their opacity – while reaching state-of-the-art performances. Unlike its competitors, Shallow2Deep promotes variability of localised structures in NN, helping to reduce NN opacity. The proposed work analyses the role of local variability in NN architectures design, presenting experimental results that show how this feature is actually desirable
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific
method to answer questions about why natural phenomena occur and to make
testable models that explain the phenomena. Discovering equations, laws and
principles that are invariant, robust and causal explanations of the world has
been fundamental in physical sciences throughout the centuries. Discoveries
emerge from observing the world and, when possible, performing interventional
studies in the system under study. With the advent of big data and the use of
data-driven methods, causal and equation discovery fields have grown and made
progress in computer science, physics, statistics, philosophy, and many applied
fields. All these domains are intertwined and can be used to discover causal
relations, physical laws, and equations from observational data. This paper
reviews the concepts, methods, and relevant works on causal and equation
discovery in the broad field of Physics and outlines the most important
challenges and promising future lines of research. We also provide a taxonomy
for observational causal and equation discovery, point out connections, and
showcase a complete set of case studies in Earth and climate sciences, fluid
dynamics and mechanics, and the neurosciences. This review demonstrates that
discovering fundamental laws and causal relations by observing natural
phenomena is being revolutionised with the efficient exploitation of
observational data, modern machine learning algorithms and the interaction with
domain knowledge. Exciting times are ahead with many challenges and
opportunities to improve our understanding of complex systems.Comment: 137 page
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedia
processing and machine learning has marked a new era for edge and cloud
computing. These applications involve massive data and compute-intensive tasks,
and thus, typical computing paradigms in embedded systems and data centers are
stressed to meet the worldwide demand for high performance. Concurrently, the
landscape of the semiconductor field in the last 15 years has constituted power
as a first-class design concern. As a result, the community of computing
systems is forced to find alternative design approaches to facilitate
high-performance and/or power-efficient computing. Among the examined
solutions, Approximate Computing has attracted an ever-increasing interest,
with research works applying approximations across the entire traditional
computing stack, i.e., at software, hardware, and architectural levels. Over
the last decade, there is a plethora of approximation techniques in software
(programs, frameworks, compilers, runtimes, languages), hardware (circuits,
accelerators), and architectures (processors, memories). The current article is
Part I of our comprehensive survey on Approximate Computing, and it reviews its
motivation, terminology and principles, as well it classifies and presents the
technical details of the state-of-the-art software and hardware approximation
techniques.Comment: Under Review at ACM Computing Survey
Analysis of Parkinson's Disease Gait using Computational Intelligence
Millions of individuals throughout the world are living with Parkinson’s disease (PD), a neurodegenerative condition whose symptoms are difficult to differentiate from those of other disorders. Freezing of gait (FOG) is one of the signs of Parkinson’s disease that have been utilized as the main diagnostic factor. Bradykinesia, tremors, depression, hallucinations, cognitive impairment, and falls are all common symptoms of Parkinson’s disease (PD). This research uses a dataset that captures data on individuals with PD who suffer from freezing of gait. This dataset includes data for medication in both the “On” and “Off” stages (denoting whether patients have taken their medicines or not). The dataset is comprised of four separate experiments, which are referred to as Voluntary Stop, Timed Up and Go (TUG), Simple Motor Task, and Dual Motor and Cognitive Task. Each of these tests has been carried out over a total of three separate attempts (trials) to verify that they are both reliable and accurate. The dataset was used for four significant challenges. The first challenge is to differentiate between people with Parkinson’s disease and healthy volunteers, and the second task is to evaluate effectiveness of medicines on the patients. The third task is to detect episodes of FOG in each individual, and the last task is to predict the FOG episode at the time of occurrence. For the last task, the author proposed. a new framework to make real-time predictions for detecting FOG, in which the results demonstrated the effectiveness of the approach. It is worth mentioning that techniques from many classifiers have been combined in order to reduce the likelihood of being biased toward a single approach. Multilayer Perceptron, K-Nearest Neighbors, random Forest, and Decision Tree Classifier all produced the best results when applied to the first three tasks with an accuracy of more than 90% amongst the classifiers that were investigated
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