Multi Processor Systems On Chip with Configurable Hardware Acceleration

The evolution of the electronics embedded applications forces electronics systems designers to match their ever increasing requirements. This evolution pushes the computational power of digital signal processing systems, as well as the energy required to accomplish the computations, due to the increasing mobility of such applications. Current approaches used to match these requirements relies on the adoption of application specific signal processors. Such kind of devices exploits powerful accelerators, which are able to match both performance and energy requirements. On the other hand, the too high specificity of such accelerators often results in a lack of flexibility which affects non-recurrent engineering costs, time to market, and market volumes too. The state of the art mainly proposes two solutions to overcome these issues with the ambition of delivering reasonable performance and energy efficiency: reconfigurable computing and multi-processors computing. All of these solutions benefits from the post-fabrication programmability, that definitively results in an increased flexibility. Nevertheless, the gap between these approaches and dedicated hardware is still too high for many application domains, especially when targeting the mobile world. In this scenario, flexible and energy efficient acceleration can be achieved by merging these two computational paradigms, in order to address all the above introduced constraints. This thesis focuses on the exploration of the design and application spectrum of reconfigurable computing, exploited as application specific accelerators for multi-processors systems on chip. More specifically, it introduces a reconfigurable digital signal processor featuring a heterogeneous set of reconfigurable engines, and a homogeneous multi-core system, exploiting three different flavours of reconfigurable and mask-programmable technologies as implementation platform for applications specific accelerators. In this work, the various trade-offs concerning the utilization multi-core platforms and the different configuration technologies are explored, characterizing the design space of the proposed approach in terms of programmability, performance, energy efficiency and manufacturing costs.

Design and implementation of a novel multi-constellation FPGA-based dual frequency GNSS receiver for space applications

The PhD activity described in the document is part of the Microsatellite and Microsystem Laboratory of the II Faculty of Engineering, University of Bologna. The main objective is the design and development of a GNSS receiver for the orbit determination of microsatellites in low earth orbit. The development starts from the electronic design and goes up to the implementation of the navigation algorithms, covering all the aspects that are involved in this type of applications. The use of GPS receivers for orbit determination is a consolidated application used in many space missions, but the development of the new GNSS system within few years, such as the European Galileo, the Chinese COMPASS and the Russian modernized GLONASS, proposes new challenges and offers new opportunities to increase the orbit determination performances. The evaluation of improvements coming from the new systems together with the implementation of a receiver that is compatible with at least one of the new systems, are the main activities of the PhD. The activities can be divided in three section: receiver requirements definition and prototype implementation, design and analysis of the GNSS signal tracking algorithms, and design and analysis of the navigation algorithms. The receiver prototype is based on a Virtex FPGA by Xilinx, and includes a PowerPC processor. The architecture follows the software defined radio paradigm, so most of signal processing is performed in software while only what is strictly necessary is done in hardware. The tracking algorithms are implemented as a combination of Phase Locked Loop and Frequency Locked Loop for the carrier, and Delay Locked Loop with variable bandwidth for the code. The navigation algorithm is based on the extended Kalman filter and includes an accurate LEO orbit model.

A numerical methodology for the multi-objective optimization of the DI Diesel engine combustion

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

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.

Geological and hydrogeological features affecting migration, multi-phase partitioning and degradation of chlorinated hydrocarbons through unconsolidated porous media.

Chlorinated solvents are the most ubiquitous organic contaminants found in groundwater since the last five decades. They generally reach groundwater as Dense Non-Aqueous Phase Liquid (DNAPL). This phase can migrate through aquifers, and also through aquitards, in ways that aqueous contaminants cannot. The complex phase partitioning to which chlorinated solvent DNAPLs can undergo (i.e. to the dissolved, vapor or sorbed phase), as well as their transformations (e.g. degradation), depend on the physico-chemical properties of the contaminants themselves and on features of the hydrogeological system. The main goal of the thesis is to provide new knowledge for the future investigations of sites contaminated by DNAPLs in alluvial settings, proposing innovative investigative approaches and emphasizing some of the key issues and main criticalities of this kind of contaminants in such a setting. To achieve this goal, the hydrogeologic setting below the city of Ferrara (Po plain, northern Italy), which is affected by scattered contamination by chlorinated solvents, has been investigated at different scales (regional and site specific), both from an intrinsic (i.e. groundwater flow systems) and specific (i.e. chlorinated solvent DNAPL behavior) point of view. Detailed investigations were carried out in particular in one selected test-site, known as “Caretti site”, where high-resolution vertical profiling of different kind of data were collected by means of multilevel monitoring systems and other innovative sampling and analytical techniques. This allowed to achieve a deep geological and hydrogeological knowledge of the system and to reconstruct in detail the architecture of contaminants in relationship to the features of the hosting porous medium. The results achieved in this thesis are useful not only at local scale, e.g. employable to interpret the origin of contamination in other sites of the Ferrara area, but also at global scale, in order to address future remediation and protection actions of similar hydrogeologic settings.

Cannabinoid system combined to classic targets for a new MTDL strategy: design and synthesis of natural inspired molecules for Alzheimer's disease

In this thesis is described the design and synthesis of potential agents for the treatment of the multifactorial Alzheimer’s disease (AD). Our multi-target approach was to consider cannabinoid system involved in AD, together with classic targets. In the first project, designed modifications were performed on lead molecule in order to increase potency and obtain balanced activities on fatty acid amide hydrolase and cholinesterases. A small library of compounds was synthesized and biological results showed increased inhibitory activity (nanomolar range) related to selected target. The second project was focused on the benzofuran framework, a privileged structure being a common moiety found in many biologically active natural products and therapeutics. Hybrid molecules were designed and synthesized, focusing on the inhibition of cholinesterases, Aβ aggregation, FAAH and on the interaction with CB receptors. Preliminary results showed that several compounds are potent CB ligands, in particular the high affinity for CB2 receptors, could open new opportunities to modulate neuroinflammation. The third and the fourth project were carried out at the IMS, Aberdeen, under the supervision of Prof. Matteo Zanda. The role of the cannabinoid system in the brain is still largely unexplored and the relationship between the CB1 receptors functional modification, density and distribution and the onset of a pathological state is not well understood. For this reasons, Rimonabant analogues suitable as radioligands were synthesized. The latter, through PET, could provide reliable measurements of density and distribution of CB1 receptors in the brain. In the fifth project, in collaboration with CHyM of York, the goal was to develop arginine analogues that are target specific due to their exclusively location into NOS enzymes and could work as MRI contrasting agents. Synthesized analogues could be suitable substrate for the transfer of polarization by p-H2 molecules through SABRE technique transforming MRI a more sensitive and faster technique.

A Web GIS Decision Support System For Coastal Risk Assessment and Mitigation Planning

Coastal flooding poses serious threats to coastal areas around the world, billions of dollars in damage to property and infrastructure, and threatens the lives of millions of people. Therefore, disaster management and risk assessment aims at detecting vulnerability and capacities in order to reduce coastal flood disaster risk. In particular, non-specialized researchers, emergency management personnel, and land use planners require an accurate, inexpensive method to determine and map risk associated with storm surge events and long-term sea level rise associated with climate change. This study contributes to the spatially evaluation and mapping of social-economic-environmental vulnerability and risk at sub-national scale through the development of appropriate tools and methods successfully embedded in a Web-GIS Decision Support System. A new set of raster-based models were studied and developed in order to be easily implemented in the Web-GIS framework with the purpose to quickly assess and map flood hazards characteristics, damage and vulnerability in a Multi-criteria approach. The Web-GIS DSS is developed recurring to open source software and programming language and its main peculiarity is to be available and usable by coastal managers and land use planners without requiring high scientific background in hydraulic engineering. The effectiveness of the system in the coastal risk assessment is evaluated trough its application to a real case study.

A multi-methodological approach for the study of polymorphism in organic pigments and pharmaceuticals

The study of polymorphism has an important role in several fields of materials science, because structural differences lead to different physico-chemical properties of the system. This PhD work was dedicated to the investigation of polymorphism in Indigo, Thioindigo and Quinacridone, as case studies among the organic pigments employed as semiconductors, and in Paracetamol, Phenytoin and Nabumetone, chosen among some commonly used API. The aim of the research was to improve the understanding on the structures of bulk crystals and thin films, adopting Raman spectroscopy as the method of choice, while resorting to other experimental techniques to complement the gathered information. Different crystalline polymorphs, in fact, may be conveniently distinguished by their Raman spectra in the region of the lattice phonons (10-150 cm-1), the frequencies of which, probing the inter-molecular interactions, are very sensitive to even slight modifications in the molecular packing. In particular, we have used Confocal Raman Microscopy, which is a powerful, yet simple, technique for the investigation of crystal polymorphism in organic and inorganic materials, being capable of monitoring physical modifications, chemical transformations and phase inhomogeneities in crystal domains at the micrometre scale. In this way, we have investigated bulk crystals and thin film samples obtained with a variety of crystal growth and deposition techniques. Pure polymorphs and samples with phase mixing were found and fully characterized. Raman spectroscopy was complemented mainly by XRD measurements for bulk crystals and by AFM, GIXD and TEM for thin films. Structures and phonons of the investigated polymorphs were computed by DFT methods, and the comparison between theoretical and experimental results was used to assess the relative stability of the polymorphs and to assist the spectroscopic investigation. The Raman measurements were thus found to be able to clarify ambiguities in the phase assignments which otherwise the other methods were unable to solve.

Distributed Large-scale Mixed-Integer Optimization with Application to Energy and Multi-robot Networks

Several decision and control tasks in cyber-physical networks can be formulated as large- scale optimization problems with coupling constraints. In these "constraint-coupled" problems, each agent is associated to a local decision variable, subject to individual constraints. This thesis explores the use of primal decomposition techniques to develop tailored distributed algorithms for this challenging set-up over graphs. We first develop a distributed scheme for convex problems over random time-varying graphs with non-uniform edge probabilities. The approach is then extended to unknown cost functions estimated online. Subsequently, we consider Mixed-Integer Linear Programs (MILPs), which are of great interest in smart grid control and cooperative robotics. We propose a distributed methodological framework to compute a feasible solution to the original MILP, with guaranteed suboptimality bounds, and extend it to general nonconvex problems. Monte Carlo simulations highlight that the approach represents a substantial breakthrough with respect to the state of the art, thus representing a valuable solution for new toolboxes addressing large-scale MILPs. We then propose a distributed Benders decomposition algorithm for asynchronous unreliable networks. The framework has been then used as starting point to develop distributed methodologies for a microgrid optimal control scenario. We develop an ad-hoc distributed strategy for a stochastic set-up with renewable energy sources, and show a case study with samples generated using Generative Adversarial Networks (GANs). We then introduce a software toolbox named ChoiRbot, based on the novel Robot Operating System 2, and show how it facilitates simulations and experiments in distributed multi-robot scenarios. Finally, we consider a Pickup-and-Delivery Vehicle Routing Problem for which we design a distributed method inspired to the approach of general MILPs, and show the efficacy through simulations and experiments in ChoiRbot with ground and aerial robots.

Unified control system for three-phase electric drives operating in magnetic saturation region

The research project aims to study and develop control techniques for a generalized three-phase and multi-phase electric drive able to efficiently manage most of the drive types available for traction application. The generalized approach is expanded to both linear and non- linear machines in magnetic saturation region starting from experimental flux characterization and applying the general inductance definition. The algorithm is able to manage fragmented drives powered from different batteries or energy sources and will be able to ensure operability even in case of faults in parts of the system. The algorithm was tested using model-in-the-loop in software environment and then applied on experimental test benches with collaboration of an external company.

High performance and energy-efficient instruction cache design and optimisation for ultra-low-power multi-core clusters

High Energy efficiency and high performance are the key regiments for Internet of Things (IoT) end-nodes. Exploiting cluster of multiple programmable processors has recently emerged as a suitable solution to address this challenge. However, one of the main bottlenecks for multi-core architectures is the instruction cache. While private caches fall into data replication and wasting area, fully shared caches lack scalability and form a bottleneck for the operating frequency. Hence we propose a hybrid solution where a larger shared cache (L1.5) is shared by multiple cores connected through a low-latency interconnect to small private caches (L1). However, it is still limited by large capacity miss with a small L1. Thus, we propose a sequential prefetch from L1 to L1.5 to improve the performance with little area overhead. Moreover, to cut the critical path for better timing, we optimized the core instruction fetch stage with non-blocking transfer by adopting a 4 x 32-bit ring buffer FIFO and adding a pipeline for the conditional branch. We present a detailed comparison of different instruction cache architectures' performance and energy efficiency recently proposed for Parallel Ultra-Low-Power clusters. On average, when executing a set of real-life IoT applications, our two-level cache improves the performance by up to 20% and loses 7% energy efficiency with respect to the private cache. Compared to a shared cache system, it improves performance by up to 17% and keeps the same energy efficiency. In the end, up to 20% timing (maximum frequency) improvement and software control enable the two-level instruction cache with prefetch adapt to various battery-powered usage cases to balance high performance and energy efficiency.

Multi-phase electric drives for mobility

Multi-phase electrical drives are potential candidates for the employment in innovative electric vehicle powertrains, in response to the request for high efficiency and reliability of this type of application. In addition to the multi-phase technology, in the last decades also, multilevel technology has been developed. These two technologies are somewhat complementary since both allow increasing the power rating of the system without increasing the current and voltage ratings of the single power switches of the inverter. In this thesis, some different topics concerning the inverter, the motor and the fault diagnosis of an electric vehicle powertrain are addressed. In particular, the attention is focused on multi-phase and multilevel technologies and their potential advantages with respect to traditional technologies. First of all, the mathematical models of two multi-phase machines, a five-phase induction machine and an asymmetrical six-phase permanent magnet synchronous machines are developed using the Vector Space Decomposition approach. Then, a new modulation technique for multi-phase multilevel T-type inverters, which solves the voltage balancing problem of the DC-link capacitors, ensuring flexible management of the capacitor voltages, is developed. The technique is based on the proper selection of the zero-sequence component of the modulating signals. Subsequently, a diagnostic technique for detecting the state of health of the rotor magnets in a six-phase permanent magnet synchronous machine is established. The technique is based on analysing the electromotive force induced in the stator windings by the rotor magnets. Furthermore, an innovative algorithm able to extend the linear modulation region for five-phase inverters, taking advantage of the multiple degrees of freedom available in multi-phase systems is presented. Finally, the mathematical model of an eighteen-phase squirrel cage induction motor is defined. This activity aims to develop a motor drive able to change the number of poles of the machine during the machine operation.

Modelling, simulation and control of rotary-wing aircraft in multi-agent scenario

The topic of this thesis is the design and the implementation of mathematical models and control system algorithms for rotary-wing unmanned aerial vehicles to be used in cooperative scenarios. The use of rotorcrafts has many attractive advantages, since these vehicles have the capability to take-off and land vertically, to hover and to move backward and laterally. Rotary-wing aircraft missions require precise control characteristics due to their unstable and heavy coupling aspects. As a matter of fact, flight test is the most accurate way to evaluate flying qualities and to test control systems. However, it may be very expensive and/or not feasible in case of early stage design and prototyping. A good compromise is made by a preliminary assessment performed by means of simulations and a reduced flight testing campaign. Consequently, having an analytical framework represents an important stage for simulations and control algorithm design. In this work mathematical models for various helicopter configurations are implemented. Different flight control techniques for helicopters are presented with theoretical background and tested via simulations and experimental flight tests on a small-scale unmanned helicopter. The same platform is used also in a cooperative scenario with a rover. Control strategies, algorithms and their implementation to perform missions are presented for two main scenarios. One of the main contributions of this thesis is to propose a suitable control system made by a classical PID baseline controller augmented with L1 adaptive contribution. In addition a complete analytical framework and the study of the dynamics and the stability of a synch-rotor are provided. At last, the implementation of cooperative control strategies for two main scenarios that include a small-scale unmanned helicopter and a rover.

Multi-organ support device for combined extracorporeal carbon dioxide removal and hemofiltration treatments with pump-free solution for ultrafiltration

The use of extracorporeal organ support (ECOS) devices is increasingly widespread, to temporarily sustain or replace the functions of impaired organs in critically ill patients. Among ECOS, respiratory functions are supplied by extracorporeal life support (ECLS) therapies like extracorporeal membrane oxygenation (ECMO) and extracorporeal carbon dioxide removal (ECCO2R), and renal replacement therapies (RRT) are used to support kidney functions. However, the leading cause of mortality in critically ill patients is multi-organ dysfunction syndrome (MODS), which requires a complex therapeutic strategy where extracorporeal treatments are often integrated to pharmacological approach. Recently, the concept of multi-organ support therapy (MOST) has been introduced, and several forms of isolated ECOS devices are sequentially connected to provide simultaneous support to different organ systems. The future of critical illness goes towards the development of extracorporeal devices offering multiple organ support therapies on demand by a single hardware platform, where treatment lines can be used alternately or in conjunction. The aim of this industrial PhD project is to design and validate a device for multi-organ support, developing an auxiliary line for renal replacement therapy (hemofiltration) to be integrated on a platform for ECCO2R. The intended purpose of the ancillary line, which can be connected on demand, is to remove excess fluids by ultrafiltration and achieve volume control by the infusion of a replacement solution, as patients undergoing respiratory support are particularly prone to develop fluid overload. Furthermore, an ultrafiltration regulation system shall be developed using a powered and software-modulated pinch-valve on the effluent line of the hemofilter, proposed as an alternative to the state-of-the-art solution with peristaltic pump.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.