A framework for enhancing process understanding using multivariate tools on commercial batch process data

EngD ThesisA lot of effort is made by pharmaceutical companies on the research and development of new pharmaceutical products and processes using the latest in quality by design tools, and process analytical technologies. Older pharmaceutical processes that were developed without the use of these tools are, however, somewhat neglected. Significant quantities of process data are routinely collected and stored but the information contained within this data is not extracted. Extensive literature on multivariate statistical process monitoring and control exists for exploring both batch and continuous process data. However, these methodologies rely on data from processes that are relatively well understood or controlled. Many industrial processes show batch to batch variability, which may be tolerated as it is not detrimental to the quality of the product, and the impact of this variability is not fully understood. The thesis presents a framework for exploring historical batch process data, to extract insights on where process control can be improved. The challenges presented with commercial process data are discussed. Multivariate tools such as multi-way principal component analysis are used to investigate variability in process data. The framework presented discusses the pre-processing steps necessary with batch process data, followed by outlier detection, and finally multivariate modelling of the data to identify where the process could benefit from improved understanding and control. This framework is demonstrated through the application to commercial process data from the active pharmaceutical drug substance manufacturing process of spironolactone at Piramal Healthcare, Morpeth, UK. In this case study, the process exhibits variability in drying times which traditional univariate data analysis has not been able to solve. The results demonstrated some of the challenges the use of the available data from commercial processes. Although the results from the multivariate data analysis did not show a significant statistical difference between the batches with long and short drying times, small differences were observed between these two groups. Further analysis of the crystallization process using infrared spectroscopic techniques which identified a potential root cause to the extended drying time.This EngD project was supported by the Engineering and Physical Sciences Research Council (EPSRC) and Piramal Healthcare, Morpeth

Multi-objective optimization and model-based predictive control using state feedback linearization for crystallization

The ongoing Quality-by-Design paradigm shift in the pharmaceutical industry has sparked a new interest in exploring advanced process control techniques to aid the efficient manufacture of high value products. One important process in the manufacturing is crystallization, a key process in purification of active pharmaceutical ingredients (APIs). There has been little crystallization control research in the area of global input/output linearization, otherwise referred to as state-feedback linearization (SFL). The global linearization allows a nonlinear model to be linearized over the whole domain for which the model is valid and can be embedded into a model predictive controller (MPC). MPC allows the control of a process based on a model which captures the physical understanding and constraints, but a widely reported challenge with the SFL technique is the poor ability of explicitly handling the plant constraints, which is not ideal for a highly regulated production environment such as pharmaceutical manufacturing. Therefore, the first purpose of this research is to explore the use of SFL and how it can be applied to controlling batch and continuous MSMPR crystallization processes with the incorporation of plant constraints in the MPC (named SFL-Plant constraints). The contribution made from this research is the exploration of the SFL MPC technique with successful implementation of SFL-Plant constraints. The novelty in this method is that the technique builds on existing SFL-MPC frameworks to incorporate a nonlinear constraints routine which handles plant constraints. The technique is applied on numerous scenarios of batch and continuous mixed suspension mixed product removal (MSMPR) supersaturation control of paracetamol in water, both seeded and unseeded, which all show that the SFL-Plant constraints technique indeed produces feasible control over crystallization subject to constraints imposed by limitations such as heat transfer. The SFL-MPC with SFL-Plant constraints was applied to single-input single-output (SISO) and multiple-input multipleoutput (MIMO) systems, demonstrating consistent success across both schemes of control. It was also determined that the SFL-Plant constraints do increase the computational demand by 2 to 5 times that of the SFL when unconstrained. However, the difference in absolute time is not so significant, typically an MPC which acted on a system each minute required less than 5 seconds of computation time with inclusion of SFL-Plant constraints. This technique 5 presents the opportunity to use the SFL-MPC with real system constraints with little additional computation effort, where otherwise this may have not been possible. A further advancement in this research is the comparison between the SFL-MPC technique to an MPC with a data-driven model - AutoRegression model with eXogenous input (ARX) – which is widely used in industry. An ARX model was identified for batch supersaturation control using a batch crystallization model of paracetamol in isopropyl alcohol (IPA) in gPROMS Formulated Products as the plant, and an ARX model developed in an industrial software for advanced process control – PharmaMV. The ARX-MPC performance was compared with SFL-MPC performance and it was found that although the ARX-MPC performed well when controlling a process which operated around the point the ARX-MPC was initially identified, the capability of tracking the supersaturation profile deteriorated when larger setpoints were targeted. SFL-MPC, on the other hand, saw some deterioration in performance quantified through an increase in output tracking error, but remained robust at tracking a wide range of supersaturation targets, thus outperforming the ARX-MPC for trajectory tracking control. Finally, single-objective and multi-objective optimization of a batch crystallization process is investigated to build on the existing techniques. Two opportunities arose from the literature review. The first was the use of variable-time decision variables in optimization, as it appears all pre-existing crystallization optimization problems to determine the ideal crystallization temperature trajectory for maximising mean-size are constructed of piecewise-constant or piecewise-continuous temperature profiles with a fixed time step. In this research the timestep was added as a decision variable to the optimization problem for each piecewise continuous ramp in the crystallization temperature profile and the results showed that for the maximisation of mean crystal length in a 300-minute batch simulation, when using 10 temperature ramps each of variable length resulted in a 20% larger mean size than that of 10 temperature ramps, each over a fixed time length. The second opportunity was to compare the performance of global evolution based Nondominated Sorting Genetic Algorithm – II (NSGA-II) with a deterministic SQP optimization method and a further hybrid approach utilising first the NSGA-II and then the SQP algorithm. It was found that for batch crystallization optimization, it is possible for the SQP to converge a global solution, and the convergence can be guaranteed in the shortest time with little compromise using the hybrid 6 method if no information is known about the process. The NSGA-II alone required excessive time to reach a solution which is less refined. Finally, a multi-objective optimization problem is formed to assess the ability to gain insight into crystallization operation when there are multiple competing objectives such as maximising the number weighted mean size and minimizing the number weighted coefficient of variation in size. The insight gained from this is that it is more time efficient to perform single-objective optimization on each objective first and then initialize the multi-objective NSGA-II algorithm with the single-objective optimal profiles, because this results in a greatly refined solution in significantly less time than if the NSGA-II algorithm was to run without initialization, the results were approximately 20% better for both mean size and coefficient of variation in 10% of the time with initialization

Latent variable modeling approaches to assist the implementation of quality-by-design paradigms in pharmaceutical development and manufacturing

With the introduction of the Quality-by-Design (QbD) initiative, the American Food and Drug Administration and the other pharmaceutical regulatory Agencies aimed to change the traditional approaches to pharmaceutical development and manufacturing. Pharmaceutical companies have been encouraged to use systematic and science-based tools for the design and control of their processes, in order to demonstrate a full understanding of the driving forces acting on them. From an engineering perspective, this initiative can be seen as the need to apply modeling tools in pharmaceutical development and manufacturing activities. The aim of this Dissertation is to show how statistical modeling, and in particular latent variable models (LVMs), can be used to assist the practical implementation of QbD paradigms to streamline and accelerate product and process design activities in pharmaceutical industries, and to provide a better understanding and control of pharmaceutical manufacturing processes. Three main research areas are explored, wherein LVMs can be applied to support the practical implementation of the QbD paradigms: process understanding, product and process design, and process monitoring and control. General methodologies are proposed to guide the use of LVMs in different applications, and their effectiveness is demonstrated by applying them to industrial, laboratory and simulated case studies. With respect to process understanding, a general methodology for the use of LVMs is proposed to aid the development of continuous manufacturing systems. The methodology is tested on an industrial process for the continuous manufacturing of tablets. It is shown how LVMs can model jointly data referred to different raw materials and different units in the production line, allowing to understand which are the most important driving forces in each unit and which are the most critical units in the line. Results demonstrate how raw materials and process parameters impact on the intermediate and final product quality, enabling to identify paths along which the process moves depending on its settings. This provides a tool to assist quality risk assessment activities and to develop the control strategy for the process. In the area of product and process design, a general framework is proposed for the use of LVM inversion to support the development of new products and processes. The objective of model inversion is to estimate the best set of inputs (e.g., raw material properties, process parameters) that ensure a desired set of outputs (e.g., product quality attributes). Since the inversion of an LVM may have infinite solutions, generating the so-called null space, an optimization framework allowing to assign the most suitable objectives and constraints is used to select the optimal solution. The effectiveness of the framework is demonstrated in an industrial particle engineering problem to design the raw material properties that are needed to produce granules with desired characteristics from a high-shear wet granulation process. Results show how the framework can be used to design experiments for new products design. The analogy between the null space and the Agencies’ definition of design space is also demonstrated and a strategy to estimate the uncertainties in the design and in the null space determination is provided. The proposed framework for LVM inversion is also applied to assist the design of the formulation for a new product, namely the selection of the best excipient type and amount to mix with a given active pharmaceutical ingredient (API) to obtain a blend of desired properties. The optimization framework is extended to include constraints on the material selection, the API dose or the final tablet weight. A user-friendly interface is developed to aid formulators in providing the constraints and objectives of the problem. Experiments performed industrially on the formulation designed in-silico confirm that model predictions are in good agreement with the experimental values. LVM inversion is shown to be useful also to address product transfer problems, namely the problem of transferring the manufacturing of a product from a source plant, wherein most of the experimentation has been carried out, to a target plant which may differ for size, lay-out or involved units. An experimental process for pharmaceutical nanoparticles production is used as a test bed. An LVM built on different plant data is inverted to estimate the most suitable process conditions in a target plant to produce nanoparticles of desired mean size. Experiments designed on the basis of the proposed LVM inversion procedure demonstrate that the desired nanoparticles sizes are obtained, within experimental uncertainty. Furthermore, the null space concept is validated experimentally. Finally, with respect to the process monitoring and control area, the problem of transferring monitoring models between different plants is studied. The objective is to monitor a process in a target plant where the production is being started (e.g., a production plant) by exploiting the data available from a source plant (e.g., a pilot plant). A general framework is proposed to use LVMs to solve this problem. Several scenarios are identified on the basis of the available information, of the source of data and on the type of variables to include in the model. Data from the different plants are related through subsets of variables (common variables) measured in both plants, or through plant-independent variables obtained from conservation balances (e.g., dimensionless numbers). The framework is applied to define the process monitoring model for an industrial large-scale spray-drying process, using data available from a pilot-scale process. The effectiveness of the transfer is evaluated in terms of monitoring performances in the detection of a real fault occurring in the target process. The proposed methodologies are then extended to batch systems, considering a simulated penicillin fermentation process. In both cases, results demonstrate that the transfer of knowledge from the source plant enables better monitoring performances than considering only the data available from the target plant

Technology in conservation: towards a system for in-field drone detection of invasive vegetation

Remote sensing can assist in monitoring the spread of invasive vegetation. The adoption of camera-carrying unmanned aerial vehicles, commonly referred to as drones, as remote sensing tools has yielded images of higher spatial resolution than traditional techniques. Drones also have the potential to interact with the environment through the delivery of bio-control or herbicide, as seen with their adoption in precision agriculture. Unlike in agricultural applications, however, invasive plants do not have a predictable position relative to each other within the environment. To facilitate the adoption of drones as an environmental monitoring and management tool, drones need to be able to intelligently distinguish between invasive and non-invasive vegetation on the fly. In this thesis, we present the augmentation of a commercially available drone with a deep machine learning model to investigate the viability of differentiating between an invasive shrub and other vegetation. As a case study, this was applied to the shrub genus Hakea, originating in Australia and invasive in several countries including South Africa. However, for this research, the methodology is important, rather than the chosen target plant. A dataset was collected using the available drone and manually annotated to facilitate the supervised training of the model. Two approaches were explored, namely, classification and semantic segmentation. For each of these, several models were trained and evaluated to find the optimal one. The chosen model was then interfaced with the drone via an Android application on a mobile device and its performance was preliminarily evaluated in the field. Based on these findings, refinements were made and thereafter a thorough field evaluation was performed to determine the best conditions for model operation. Results from the classification task show that deep learning models are capable of distinguishing between target and other shrubs in ideal candidate windows. However, classification in this manner is restricted by the proposal of such candidate windows. End-to-end image segmentation using deep learning overcomes this problem, classifying the image in a pixel-wise manner. Furthermore, the use of appropriate loss functions was found to improve model performance. Field tests show that illumination and shadow pose challenges to the model, but that good recall can be achieved when the conditions are ideal. False positive detection remains an issue that could be improved. This approach shows the potential for drones as an environmental monitoring and management tool when coupled with deep machine learning techniques and outlines potential problems that may be encountered

Technology in conservation: towards a system for in-field drone detection of invasive vegetation

Remote sensing can assist in monitoring the spread of invasive vegetation. The adoption of camera-carrying unmanned aerial vehicles, commonly referred to as drones, as remote sensing tools has yielded images of higher spatial resolution than traditional techniques. Drones also have the potential to interact with the environment through the delivery of bio-control or herbicide, as seen with their adoption in precision agriculture. Unlike in agricultural applications, however, invasive plants do not have a predictable position relative to each other within the environment. To facilitate the adoption of drones as an environmental monitoring and management tool, drones need to be able to intelligently distinguish between invasive and non-invasive vegetation on the fly. In this thesis, we present the augmentation of a commercially available drone with a deep machine learning model to investigate the viability of differentiating between an invasive shrub and other vegetation. As a case study, this was applied to the shrub genus Hakea, originating in Australia and invasive in several countries including South Africa. However, for this research, the methodology is important, rather than the chosen target plant. A dataset was collected using the available drone and manually annotated to facilitate the supervised training of the model. Two approaches were explored, namely, classification and semantic segmentation. For each of these, several models were trained and evaluated to find the optimal one. The chosen model was then interfaced with the drone via an Android application on a mobile device and its performance was preliminarily evaluated in the field. Based on these findings, refinements were made and thereafter a thorough field evaluation was performed to determine the best conditions for model operation. Results from the classification task show that deep learning models are capable of distinguishing between target and other shrubs in ideal candidate windows. However, classification in this manner is restricted by the proposal of such candidate windows. End-to-end image segmentation using deep learning overcomes this problem, classifying the image in a pixel-wise manner. Furthermore, the use of appropriate loss functions was found to improve model performance. Field tests show that illumination and shadow pose challenges to the model, but that good recall can be achieved when the conditions are ideal. False positive detection remains an issue that could be improved. This approach shows the potential for drones as an environmental monitoring and management tool when coupled with deep machine learning techniques and outlines potential problems that may be encountered

Advanced adaptive modelling approaches in the evolution of vector/cell manufacturing processes

PhD ThesisThe field of cell gene therapy has seen significant progress in recent years. The last decade has seen the licensing of the first Cell Gene Therapy (CGT) treatments in Europe and clinical trials have demonstrated safety and efficacy in the treatment of numerous severe inherited diseases of the blood, immune and nervous systems. Specifically, autologous viral vector-based CGT treatments have been the most successful to date. However, the manufacturing processes for these CGT treatments are at an early stage of development, and high levels of complexity, process variability and a lack of advanced process and product understanding in vector/cell manufacturing are hindering the development of new processes and treatments. Here, Multivariate Data Analysis (MVDA) and Machine Learning (ML) techniques, which have not yet been widely exploited for the development of CGT processes, were leveraged to address some of the main hurdles in the development and optimisation of CGT processes. Principal component analysis (PCA) was primarily used for feature extraction to understand the main correlations and sources of variability within the process data, and to evaluate the similarities and differences between batches. Additionally, a sparse PCA algorithm was developed to ease the interpretation of the principal components with a large number of variables present in the dataset. Predictive modelling techniques were utilized to model the relationships between process variables and critical quality attributes (CQAs) of the viral vector and cell drug products. The infectious titres of lentiviral vector (LV) products from both adherent cell cultures and suspension cell cultures were modelled and predicted successfully and critical process variables were identified with statistically significant correlations to this CQA. In cell drug product manufacturing, the LV copy number in the patient’s transduced cells was also modelled and process parameters in LV manufacturing and cell drug product manufacturing were linked to this CQA. Overall, the modelling process recovered valuable information from historical process data from the early stages of process development. This data frequently remains unexploited, due to its commonly truncated and unstructured nature; however, this work showed that MVDA/ML techniques can yield beneficial insights despite less than ideal data structure and features.GlaxoSmithKline and the Engineering and Physical Sciences Research Counci

Pharmaceutical development and manufacturing in a Quality by Design perspective: methodologies for design space description

In the last decade, the pharmaceutical industry has been experiencing a period of drastic change in the way new products and processes are being conceived, due to the introduction of the Quality by design (QbD) initiative put forth by the pharmaceutical regulatory agencies (such as the Food and Drug Adminstration (FDA) and the European Medicines Agency (EMA)). One of the most important aspects introduced in the QbD framework is that of design space (DS) of a pharmaceutical product, defined as “the multidimensional combination and interaction of input variables (e.g. material attributes) and process parameters that have been demonstrated to provide assurance of quality”. The identification of the DS represents a key advantage for pharmaceutical companies, since once the DS has been approved by the regulatory agency, movements within the DS do not constitute a manufacturing change and therefore do not require any further regulatory post-approval. This translates into an enhanced flexibility during process operation, with significant advantages in terms of productivity and process economics. Mathematical modeling, both first-principles and data-driven, has proven to be a valuable tool to assist a DS identification exercise. The development of advanced mathematical techniques for the determination and maintenance of a design space, as well as the quantification of the uncertainty associated with its identification, is a research area that has gained increasing attention during the last years. The objective of this Dissertation is to develop novel methodologies to assist the (i) determination of the design space of a new pharmaceutical product, (ii) quantify the assurance of quality for a new pharmaceutical product as advocated by the regulatory agencies, (iii) adapt and maintain a design space during plant operation, and (iv) design optimal experiments for the calibration of first-principles mathematical models to be used for design space identification. With respect to the issue of design space determination, a methodology is proposed that combines surrogate-based feasibility analysis and latent-variable modeling for the identification of the design space of a new pharmaceutical product. Projection onto latent structures (PLS) is exploited to obtain a latent representation of the space identified by the model inputs (i.e. raw material properties and process parameters) and surrogate-based feasibility is then used to reconstruct the boundary of the DS on this latent representation, with significant reduction of the overall computational burden. The final result is a compact representation of the DS that can be easily expressed in terms of the original physically-relevant input variables (process parameters and raw material properties) and can then be easily interpreted by industrial practitioners. As regards the quantification of “assurance” of quality, two novel methodologies are proposed to account for the two most common sources of model uncertainty (structural and parametric) in the model-based identification of the DS of a new pharmaceutical product. The first methodology is specifically suited for the quantification of assurance of quality when a PLS model is to be used for DS identification. Two frequentist analytical models are proposed to back-propagate the uncertainty from the quality attributes of the final product to the space identified by the set of raw material properties and process parameters of the manufacturing process. It is shown how these models can be used to identify a subset of input combinations (i.e., raw material properties and process parameters) within which the DS is expected to lie with a given degree of confidence. It is also shown how this reduced space of input combinations (called experiment space) can be used to tailor an experimental campaign for the final assessment of the DS, with a significant reduction of the experimental effort required with respect to a non-tailored experimental campaign. The validity of the proposed methodology is tested on granulation and roll compaction processes, involving both simulated and experimental data. The second methodology proposes a joint Bayesian/latent-variable approach, and the assurance of quality is quantified in terms of the probability that the final product will meet its specifications. In this context, the DS is defined in a probabilistic framework as the set of input combinations that guarantee that the probability that the product will meet its quality specifications is greater than a predefined threshold value. Bayesian multivariate linear regression is coupled with latent-variable modeling in order to obtain a computationally friendly implementation of this probabilistic DS. Specifically, PLS is exploited to reduce the computational burden for the discretization of the input domain and to give a compact representation of the DS. On the other hand, Bayesian multivariate linear regression is used to compute the probability that the product will meet the desired quality for each of the discretization points of the input domain. The ability of the methodology to give a scientifically-driven representation of the probabilistic DS is proved with three case studies involving literature experimental data of pharmaceutical unit operations. With respect to the issue of the maintenance of a design space, a methodology is proposed to adapt in real time a model-based representation of a design space during plant operation in the presence of process-model mismatch. Based on the availability of a first-principles model (FPM) or semi-empirical model for the manufacturing process, together with measurements from plant sensors, the methodology jointly exploits (i) a dynamic state estimator and (ii) feasibility analysis to perform a risk-based online maintenance of the DS. The state estimator is deployed to obtain an up-to-date FPM by adjusting in real-time a small subset of the model parameters. Feasibility analysis and surrogate-based feasibility analysis are used to update the DS in real-time by exploiting the up-to-date FPM returned by the state estimator. The effectiveness of the methodology is shown with two simulated case studies, namely the roll compaction of microcrystalline cellulose and the penicillin fermentation in a pilot scale bioreactor. As regards the design of optimal experiments for the calibration of mathematical models for DS identification, a model-based design of experiments (MBDoE) approach is presented for an industrial freeze-drying process. A preliminary analysis is performed to choose the most suitable process model between different model alternatives and to test the structural consistency of the chosen model. A new experiment is then designed based on this model using MBDoE techniques, in order to increase the precision of the estimates of the most influential model parameters. The results of the MBDoE activity are then tested both in silico and on the real equipment

Machine Learning for Resource-Constrained Computing Systems

Die verfügbaren Ressourcen in Informationsverarbeitungssystemen wie Prozessoren sind in der Regel eingeschränkt. Das umfasst z. B. die elektrische Leistungsaufnahme, den Energieverbrauch, die Wärmeabgabe oder die Chipfläche. Daher ist die Optimierung der Verwaltung der verfügbaren Ressourcen von größter Bedeutung, um Ziele wie maximale Performanz zu erreichen. Insbesondere die Ressourcenverwaltung auf der Systemebene hat über die (dynamische) Zuweisung von Anwendungen zu Prozessorkernen und über die Skalierung der Spannung und Frequenz (dynamic voltage and frequency scaling, DVFS) einen großen Einfluss auf die Performanz, die elektrische Leistung und die Temperatur während der Ausführung von Anwendungen. Die wichtigsten Herausforderungen bei der Ressourcenverwaltung sind die hohe Komplexität von Anwendungen und Plattformen, unvorhergesehene (zur Entwurfszeit nicht bekannte) Anwendungen oder Plattformkonfigurationen, proaktive Optimierung und die Minimierung des Laufzeit-Overheads. Bestehende Techniken, die auf einfachen Heuristiken oder analytischen Modellen basieren, gehen diese Herausforderungen nur unzureichend an. Aus diesem Grund ist der Hauptbeitrag dieser Dissertation der Einsatz maschinellen Lernens (ML) für Ressourcenverwaltung. ML-basierte Lösungen ermöglichen die Bewältigung dieser Herausforderungen durch die Vorhersage der Auswirkungen potenzieller Entscheidungen in der Ressourcenverwaltung, durch Schätzung verborgener (unbeobachtbarer) Eigenschaften von Anwendungen oder durch direktes Lernen einer Ressourcenverwaltungs-Strategie. Diese Dissertation entwickelt mehrere neuartige ML-basierte Ressourcenverwaltung-Techniken für verschiedene Plattformen, Ziele und Randbedingungen. Zunächst wird eine auf Vorhersagen basierende Technik zur Maximierung der Performanz von Mehrkernprozessoren mit verteiltem Last-Level Cache und limitierter Maximaltemperatur vorgestellt. Diese verwendet ein neuronales Netzwerk (NN) zur Vorhersage der Auswirkungen potenzieller Migrationen von Anwendungen zwischen Prozessorkernen auf die Performanz. Diese Vorhersagen erlauben die Bestimmung der bestmöglichen Migration und ermöglichen eine proaktive Verwaltung. Das NN ist so trainiert, dass es mit unbekannten Anwendungen und verschiedenen Temperaturlimits zurechtkommt. Zweitens wird ein Boosting-Verfahren zur Maximierung der Performanz homogener Mehrkernprozessoren mit limitierter Maximaltemperatur mithilfe von DVFS vorgestellt. Dieses basiert auf einer neuartigen {Boostability}-Metrik, die die Abhängigkeiten von Performanz, elektrischer Leistung und Temperatur auf Spannungs/Frequenz-Änderungen in einer Metrik vereint. % ignorerepeated Die Abhängigkeiten von Performanz und elektrischer Leistung hängen von der Anwendung ab und können zur Laufzeit nicht direkt beobachtet (gemessen) werden. Daher wird ein NN verwendet, um diese Werte für unbekannte Anwendungen zu schätzen und so die Komplexität der Boosting-Optimierung zu bewältigen. Drittens wird eine Technik zur Temperaturminimierung von heterogenen Mehrkernprozessoren mit Quality of Service-Zielen vorgestellt. Diese verwendet Imitationslernen, um eine Migrationsstrategie von Anwendungen aus optimalen Orakel-Demonstrationen zu lernen. Dafür wird ein NN eingesetzt, um die Komplexität der Plattform und des Anwendungsverhaltens zu bewältigen. Die Inferenz des NNs wird mit Hilfe eines vorhandenen generischen Beschleunigers, einer Neural Processing Unit (NPU), beschleunigt. Auch die ML Algorithmen selbst müssen auch mit begrenzten Ressourcen ausgeführt werden. Zuletzt wird eine Technik für ressourcenorientiertes Training auf verteilten Geräten vorgestellt, um einen konstanten Trainingsdurchsatz bei sich schnell ändernder Verfügbarkeit von Rechenressourcen aufrechtzuerhalten, wie es z.~B.~aufgrund von Konflikten bei gemeinsam genutzten Ressourcen der Fall ist. Diese Technik verwendet Structured Dropout, welches beim Training zufällige Teile des NNs auslässt. Dadurch können die erforderlichen Ressourcen für das Training dynamisch angepasst werden -- mit vernachlässigbarem Overhead, aber auf Kosten einer langsameren Trainingskonvergenz. Die Pareto-optimalen Dropout-Parameter pro Schicht des NNs werden durch eine Design Space Exploration bestimmt. Evaluierungen dieser Techniken werden sowohl in Simulationen als auch auf realer Hardware durchgeführt und zeigen signifikante Verbesserungen gegenüber dem Stand der Technik, bei vernachlässigbarem Laufzeit-Overhead. Zusammenfassend zeigt diese Dissertation, dass ML eine Schlüsseltechnologie zur Optimierung der Verwaltung der limitierten Ressourcen auf Systemebene ist, indem die damit verbundenen Herausforderungen angegangen werden