Use of shared memory in the context of embedded multi-core processors: exploration of the technology and its limits

Modern embedded systems embrace many-core shared-memory designs. Due to constrained power and area budgets, most of them feature software-managed scratchpad memories instead of data caches to increase the data locality. It is therefore programmers’ responsibility to explicitly manage the memory transfers, and this make programming these platform cumbersome. Moreover, complex modern applications must be adequately parallelized before they can the parallel potential of the platform into actual performance. To support this, programming languages were proposed, which work at a high level of abstraction, and rely on a runtime whose cost hinders performance, especially in embedded systems, where resources and power budget are constrained. This dissertation explores the applicability of the shared-memory paradigm on modern many-core systems, focusing on the ease-of-programming. It focuses on OpenMP, the de-facto standard for shared memory programming. In a first part, the cost of algorithms for synchronization and data partitioning are analyzed, and they are adapted to modern embedded many-cores. Then, the original design of an OpenMP runtime library is presented, which supports complex forms of parallelism such as multi-level and irregular parallelism. In the second part of the thesis, the focus is on heterogeneous systems, where hardware accelerators are coupled to (many-)cores to implement key functional kernels with orders-of-magnitude of speedup and energy efficiency compared to the “pure software” version. However, three main issues rise, namely i) platform design complexity, ii) architectural scalability and iii) programmability. To tackle them, a template for a generic hardware processing unit (HWPU) is proposed, which share the memory banks with cores, and the template for a scalable architecture is shown, which integrates them through the shared-memory system. Then, a full software stack and toolchain are developed to support platform design and to let programmers exploiting the accelerators of the platform. The OpenMP frontend is extended to interact with it.

Characterization of optical transduction-based molecular systems and nanoparticles for the development of chemical sensors

With the increasing importance that nanotechnologies have in everyday life, it is not difficult to realize that also a single molecule, if properly designed, can be a device able to perform useful functions: such a chemical species is called chemosensor, that is a molecule of abiotic origin that signals the presence of matter or energy. Signal transduction is the mechanism by which an interaction of a sensor with an analyte yields a measurable form of energy. When dealing with the design of a chemosensor, we need to take into account a “communication requirement” between its three component: the receptor unit, responsible for the selective analyte binding, the spacer, which controls the geometry of the system and modulates the electronic interaction between the receptor and the signalling unit, whose physico-chemical properties change upon complexation. A luminescent chemosensor communicates a variation of the physico-chemical properties of the receptor unit with a luminescence output signal. This thesis work consists in the characterization of new molecular and nanoparticle-based system which can be used as sensitive materials for the construction of new optical transduction devices able to provide information about the concentration of analytes in solution. In particular two direction were taken. The first is to continue in the development of new chemosensors, that is the first step for the construction of reliable and efficient devices, and in particular the work will be focused on chemosensors for metal ions for biomedical and environmental applications. The second is to study more efficient and complex organized systems, such as derivatized silica nanoparticles. These system can potentially have higher sensitivity than molecular systems, and present many advantages, like the possibility to be ratiometric, higher Stokes shifts and lower signal-to-noise ratio.

Parallel Architectures for Many-Core Systems-On-Chip in Deep Sub-Micron Technology

Despite the several issues faced in the past, the evolutionary trend of silicon has kept its constant pace. Today an ever increasing number of cores is integrated onto the same die. Unfortunately, the extraordinary performance achievable by the many-core paradigm is limited by several factors. Memory bandwidth limitation, combined with inefficient synchronization mechanisms, can severely overcome the potential computation capabilities. Moreover, the huge HW/SW design space requires accurate and flexible tools to perform architectural explorations and validation of design choices. In this thesis we focus on the aforementioned aspects: a flexible and accurate Virtual Platform has been developed, targeting a reference many-core architecture. Such tool has been used to perform architectural explorations, focusing on instruction caching architecture and hybrid HW/SW synchronization mechanism. Beside architectural implications, another issue of embedded systems is considered: energy efficiency. Near Threshold Computing is a key research area in the Ultra-Low-Power domain, as it promises a tenfold improvement in energy efficiency compared to super-threshold operation and it mitigates thermal bottlenecks. The physical implications of modern deep sub-micron technology are severely limiting performance and reliability of modern designs. Reliability becomes a major obstacle when operating in NTC, especially memory operation becomes unreliable and can compromise system correctness. In the present work a novel hybrid memory architecture is devised to overcome reliability issues and at the same time improve energy efficiency by means of aggressive voltage scaling when allowed by workload requirements. Variability is another great drawback of near-threshold operation. The greatly increased sensitivity to threshold voltage variations in today a major concern for electronic devices. We introduce a variation-tolerant extension of the baseline many-core architecture. By means of micro-architectural knobs and a lightweight runtime control unit, the baseline architecture becomes dynamically tolerant to variations.

Type Systems for Distributed Programs: Components and Sessions

Modern software systems, in particular distributed ones, are everywhere around us and are at the basis of our everyday activities. Hence, guaranteeing their cor- rectness, consistency and safety is of paramount importance. Their complexity makes the verification of such properties a very challenging task. It is natural to expect that these systems are reliable and above all usable. i) In order to be reliable, compositional models of software systems need to account for consistent dynamic reconfiguration, i.e., changing at runtime the communication patterns of a program. ii) In order to be useful, compositional models of software systems need to account for interaction, which can be seen as communication patterns among components which collaborate together to achieve a common task. The aim of the Ph.D. was to develop powerful techniques based on formal methods for the verification of correctness, consistency and safety properties related to dynamic reconfiguration and communication in complex distributed systems. In particular, static analysis techniques based on types and type systems appeared to be an adequate methodology, considering their success in guaranteeing not only basic safety properties, but also more sophisticated ones like, deadlock or livelock freedom in a concurrent setting. The main contributions of this dissertation are twofold. i) On the components side: we design types and a type system for a concurrent object-oriented calculus to statically ensure consistency of dynamic reconfigurations related to modifications of communication patterns in a program during execution time. ii) On the communication side: we study advanced safety properties related to communication in complex distributed systems like deadlock-freedom, livelock- freedom and progress. Most importantly, we exploit an encoding of types and terms of a typical distributed language, session π-calculus, into the standard typed π- calculus, in order to understand their expressive power.

Mental states monitoring through passive Brain-Computer Interface systems

The monitoring of cognitive functions aims at gaining information about the current cognitive state of the user by decoding brain signals. In recent years, this approach allowed to acquire valuable information about the cognitive aspects regarding the interaction of humans with external world. From this consideration, researchers started to consider passive application of brain–computer interface (BCI) in order to provide a novel input modality for technical systems solely based on brain activity. The objective of this thesis is to demonstrate how the passive Brain Computer Interfaces (BCIs) applications can be used to assess the mental states of the users, in order to improve the human machine interaction. Two main studies has been proposed. The first one allows to investigate whatever the Event Related Potentials (ERPs) morphological variations can be used to predict the users’ mental states (e.g. attentional resources, mental workload) during different reactive BCI tasks (e.g. P300-based BCIs), and if these information can predict the subjects’ performance in performing the tasks. In the second study, a passive BCI system able to online estimate the mental workload of the user by relying on the combination of the EEG and the ECG biosignals has been proposed. The latter study has been performed by simulating an operative scenario, in which the occurrence of errors or lack of performance could have significant consequences. The results showed that the proposed system is able to estimate online the mental workload of the subjects discriminating three different difficulty level of the tasks ensuring a high reliability.

Synthetic strategies of new spin-labelled supramolecular systems

Molecular recognition and self-assembly represent fundamental issues for the construction of supramolecular systems, structures in which the components are held together through non-covalent interactions. The study of host-guest complexes and mechanical interlocked molecules, important examples in this field, is necessary in order to characterize self-assembly processes, achieve more control over the molecular organization and develop sophisticated structures by using properly designed building blocks. The introduction of paramagnetic species, or spin labelling, represents an attractive opportunity that allows their detection and characterization by the Electron Spin Resonance spectroscopy, a valuable technique that provides additional information to those obtained by traditional methods. In this Thesis, recent progresses in the design and the synthesis of new paramagnetic host-guest complexes and rotaxanes characterized by the presence of nitroxide radicals and their investigation by ESR spectroscopy are reported. In Chapter 1 a brief overview of the principal concepts of supramolecular chemistry, the spin labelling approach and the development of ESR methods applied to paramagnetic systems are described. Chapter 2 and 3 are focused on the introduction of radicals in macrocycles as Cucurbiturils and Pillar[n]arenes, due to the interesting binding properties and the potential employment in rotaxanes, in order to investigate their structures and recognition properties. Chapter 4 deals with one of the most studied mechanical interlocked molecules, the bistable [2]rotaxane reported by Stoddart and Heath based on the ciclobis (paraquat-p-phenylene) CBPQT4+, that represents a well known example of molecular switch driven by external stimuli. The spin labelling of analogous architectures allows the monitoring by ESR spectroscopy of the switch mechanism involving the ring compound by tuning the spin exchange interaction. Finally, Chapter 5 contains the experimental procedures used for the synthesis of some of the compounds described in Chapter 2-4.

Synthesis and conformational analysis of heteroaromatic atropisomeric systems

The research work reported in this Thesis was held along two main lines of research. The first and main line of research is about the synthesis of heteroaromatic compounds with increasing steric hindrance, with the aim of preparing stable atropisomers. The main tools used for the study of these dynamic systems, as described in the Introduction, are DNMR, coupled with line shape simulation and DFT calculations, aimed to the conformational analysis for the prediction of the geometries and energy barriers to the trasition states. This techniques have been applied to the research projects about: • atropisomers of arylmaleimides; • atropisomers of 4-arylpyrazolo[3,4-b]pyridines; • study of the intramolecular NO2/CO interaction in solution; • study on 2-arylpyridines. Parallel to the main project, in collaboration with other groups, the research line about determination of the absolute configuration was followed. The products, deriving form organocatalytic reactions, in many cases couldn’t be analyzed by means of X-Ray diffraction, making necessary the development of a protocol based on spectroscopic methodologies: NMR, circular dichroism and computational tools (DFT, TD-DFT) have been implemented in this scope. In this Thesis are reported the determination of the absolute configuration of: • substituted 1,2,3,4-tetrahydroquinolines; • compounds from enantioselective Friedel-Crafts alkylation-acetalization cascade of naphthols with α,β-unsaturated cyclic ketones; • substituted 3,4-annulated indoles.

Improvement of The Driver Simulator Control and Comparison Between Driver-Road-Vehicle Interaction in Real and Simulated Environment

The present doctoral thesis discusses the ways to improve the performance of driving simulator, provide objective measures for the road safety evaluation methodology based on driver’s behavior and response and investigates the drivers' adaptation to the driving assistant systems. The activities are divided into two macro areas; the driving simulation studies and on-road experiments. During the driving simulation experimentation, the classical motion cueing algorithm with logarithmic scale was implemented in the 2DOF motion cueing simulator and the motion cues were found desirable by the participants. In addition, it found out that motion stimuli could change the behaviour of the drivers in terms of depth/distance perception. During the on-road experimentations, The driver gaze behaviour was investigated to find the objective measures on the visibility of the road signs and reaction time of the drivers. The sensor infusion and the vehicle monitoring instruments were found useful for an objective assessment of the pavement condition and the drivers’ performance. In the last chapter of the thesis, the safety assessment during the use of level 1 automated driving “ACC” is discussed with the simulator and on-road experiment. The drivers’ visual behaviour was investigated in both studies with innovative classification method to find the epochs of the distraction of the drivers. The behavioural adaptation to ACC showed that drivers may divert their attention away from the driving task to engage in secondary, non-driving-related tasks.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.

Flexible Computing Systems For AI Acceleration At The Extreme Edge Of The IoT

Embedding intelligence in extreme edge devices allows distilling raw data acquired from sensors into actionable information, directly on IoT end-nodes. This computing paradigm, in which end-nodes no longer depend entirely on the Cloud, offers undeniable benefits, driving a large research area (TinyML) to deploy leading Machine Learning (ML) algorithms on micro-controller class of devices. To fit the limited memory storage capability of these tiny platforms, full-precision Deep Neural Networks (DNNs) are compressed by representing their data down to byte and sub-byte formats, in the integer domain. However, the current generation of micro-controller systems can barely cope with the computing requirements of QNNs. This thesis tackles the challenge from many perspectives, presenting solutions both at software and hardware levels, exploiting parallelism, heterogeneity and software programmability to guarantee high flexibility and high energy-performance proportionality. The first contribution, PULP-NN, is an optimized software computing library for QNN inference on parallel ultra-low-power (PULP) clusters of RISC-V processors, showing one order of magnitude improvements in performance and energy efficiency, compared to current State-of-the-Art (SoA) STM32 micro-controller systems (MCUs) based on ARM Cortex-M cores. The second contribution is XpulpNN, a set of RISC-V domain specific instruction set architecture (ISA) extensions to deal with sub-byte integer arithmetic computation. The solution, including the ISA extensions and the micro-architecture to support them, achieves energy efficiency comparable with dedicated DNN accelerators and surpasses the efficiency of SoA ARM Cortex-M based MCUs, such as the low-end STM32M4 and the high-end STM32H7 devices, by up to three orders of magnitude. To overcome the Von Neumann bottleneck while guaranteeing the highest flexibility, the final contribution integrates an Analog In-Memory Computing accelerator into the PULP cluster, creating a fully programmable heterogeneous fabric that demonstrates end-to-end inference capabilities of SoA MobileNetV2 models, showing two orders of magnitude performance improvements over current SoA analog/digital solutions.

Multi-Principal Agency and the Compliance with Professional Guidelines in Health Systems: Economic and Regulatory Perspectives

Amid the trend of rising health expenditure in developed economies, changing the healthcare delivery models is an important point of action for service regulators to contain this trend. Such a change is mostly induced by either financial incentives or regulatory tools issued by the regulators and targeting service providers and patients. This creates a tripartite interaction between service regulators, professionals, and patients that manifests a multi-principal agent relationship, in which professionals are agents to two principals: regulators and patients. This thesis is concerned with such a multi-principal agent relationship in healthcare and attempts to investigate the determinants of the (non-)compliance to regulatory tools in light of this tripartite relationship. In addition, the thesis provides insights into the different institutional, economic, and regulatory settings, which govern the multi-principal agent relationship in healthcare in different countries. Furthermore, the thesis provides and empirically tests a conceptual framework of the possible determinants of (non-)compliance by physicians to regulatory tools issued by the regulator. The main findings of the thesis are first, in a multi-principal agent setting, the utilization of financial incentives to align the objectives of professionals and the regulator is important but not the only solution. This finding is based on the heterogeneity in the financial incentives provided to professionals in different health markets, which does not provide a one-size-fits-all model of financial incentives to influence clinical decisions. Second, soft law tools as clinical practice guidelines (CPGs) are important tools to mitigate the problems of the multi-principal agent setting in health markets as they reduce information asymmetries while preserving the autonomy of professionals. Third, CPGs are complex and heterogeneous and so are the determinants of (non-)compliance to them. Fourth, CPGs work but under conditions. Factors such as intra-professional competition between service providers or practitioners might lead to non-compliance to CPGs – if CPGs are likely to reduce the professional’s utility. Finally, different degrees of soft law mandate have different effects on providers’ compliance. Generally, the stronger the mandate, the stronger the compliance, however, even with a strong mandate, drivers such as intra-professional competition and co-management of patients by different professionals affected the (non-)compliance.

Synthesis and catalytic activity of new cu-based catalytic systems for the enhanced electrochemical CO2 reduction

This Ph.D. thesis concerns the synthesis of nanostructured Cu-containing materials to be used as electrode modifiers for the CO2 electroreduction in aqueous phase and the evaluation of their catalytic performances. Inspired by the fascinating concept of the artificial photosynthesis-oriented systems, several catalytic layers were electrochemically loaded on carbonaceous gas diffusion membranes, i.e., 3D structures that allow the design of eco-friendly materials for applications in green carbon recycling processes. In particular, early studies on Cu(I-II)-Cu(0) nanostructured materials were carried out to produce films on 4 cm2 sized supports by means of a fast and low-cost electrochemical procedure. Besides, through a screening of potentials, it was possible to find out a selective value for the CH3COOH production at -0.4 V vs RHE with a maximum productivity (1h reaction), ensured by the presence of the Cu+/Cu0 active redox couple (0.31 mmol gcat-1 h-1). On the basis of these results, further optimisations of the electrocatalyst chemical composition were carried out with the aim of (i) facilitating the interaction with CO2, (ii) increasing the dispersion of the catalytic active phase, and (iii) enhancing the CH3COOH productivity. To this aim, novel electrocatalysts based on layered double hydroxides (LDHs) were optimised, having as a final goal the formation of a new Cu2O-Cu0 based electrocatalyst derived from electrochemically achieved CuMgAl LDHs, subjected to calcination and reduction processes. The as-obtained electrocatalysts were tested for the selective production of CH3COOH and unprecedented results were obtained with the pristine CuMgAl LDH (2.0 mmol gcat-1 h-1). Additional characterisations of such an electrocatalyst have highlighted the possibility to achieve a ternary LDH in intimate contact with Cu2O-Cu0 species starting from the electrochemical deposition. The presence of these species, along with an alkaline environment on the electrode surface, were essential to preserve the selectivity towards the desired product, as confirmed by further operando studies.

Multiscale modeling of light-harvesting systems based on rhodamine B aggregates

Rhodamine B (RB) has been successfully exploited in the synthesis of light harvesting systems, but since RB is prone to form dimers acting as quenchers for the fluorescence, high energy transfer efficiencies can be reached only when using bulky and hydrophobic counterions acting as spacers between RBs. In this PhD thesis, a multiscale theoretical study aimed at providing insights into the structural, photophysical and optical properties of RB and its aggregates is presented. At the macroscopic level (no atomistic details) a phenomenological model describing the fluorescence decay of RB networks in presence of both quenching from dimers and exciton-exciton annihiliation is presented and analysed, showing that the quenching from dimers affects the decay only at long times, a feature that can be exploited in global fitting analysis to determine relevant chemical and photophysical information. At the mesoscopic level (atomistic details but no electronic structure) the RB aggregation in water in presence of different counterions is studied with molecular dynamics (MD) simulations. A new force field has been parametrized for describing the RB flexibility and the RB-RB interaction driving the dimerization. Simulations correctly predict the RB/counterion aggregation only in presence of bulky and hydrophobic counterion and its ability to prevent the dimerization. Finally, at the microscopic level, DFT calculations are performed to demonstrate the spacing action of bulky counterions, but standard TDDFT calculations are showed to fail in correctly describing the excited states of RB and its dimers. Moreover, also standard procedures proposed in literature for obtaining ad hoc functionals are showed to not work properly. A detailed analysis on the effect of the exact exchange shows that its short-range contribution is the crucial quantity for ameliorating results, and a new functional containing a proper amount of such an exchange is proposed and successfully tested.