SoC-based FPGA architecture for image analysis and other highly demanding applications

Al giorno d'oggi, lo sviluppo di algoritmi si concentra su calcoli efficienti in termini di prestazioni ed efficienza energetica. Tecnologie come il field programmable gate array (FPGA) e il system on chip (SoC) basato su FPGA (FPGA/SoC) hanno dimostrato la loro capacità di accelerare applicazioni di calcolo intensive risparmiando al contempo il consumo energetico, grazie alla loro capacità di elevato parallelismo e riconfigurazione dell'architettura. Attualmente, i cicli di progettazione esistenti per FPGA/SoC sono lunghi, a causa della complessità dell'architettura. Pertanto, per colmare il divario tra le applicazioni e le architetture FPGA/SoC e ottenere un design hardware efficiente per l'analisi delle immagini e altri applicazioni altamente demandanti utilizzando lo strumento di sintesi di alto livello, vengono prese in considerazione due strategie complementari: tecniche ad hoc e stima delle prestazioni. Per quanto riguarda le tecniche ad-hoc, tre applicazioni molto impegnative sono state accelerate attraverso gli strumenti HLS: discriminatore di forme di impulso per i raggi cosmici, classificazione automatica degli insetti e re-ranking per il recupero delle informazioni, sottolineando i vantaggi quando questo tipo di applicazioni viene attraversato da tecniche di compressione durante il targeting dispositivi FPGA/SoC. Inoltre, in questa tesi viene proposto uno stimatore delle prestazioni per l'accelerazione hardware per prevedere efficacemente l'utilizzo delle risorse e la latenza per FPGA/SoC, costruendo un ponte tra l'applicazione e i domini architetturali. Lo strumento integra modelli analitici per la previsione delle prestazioni e un motore design space explorer (DSE) per fornire approfondimenti di alto livello agli sviluppatori di hardware, composto da due motori indipendenti: DSE basato sull'ottimizzazione a singolo obiettivo e DSE basato sull'ottimizzazione evolutiva multiobiettivo.Nowadays, the development of algorithms focuses on performance-efficient and energy-efficient computations. Technologies such as field programmable gate array (FPGA) and system on chip (SoC) based on FPGA (FPGA/SoC) have shown their ability to accelerate intensive computing applications while saving power consumption, owing to their capability of high parallelism and reconfiguration of the architecture. Currently, the existing design cycles for FPGA/SoC are time-consuming, owing to the complexity of the architecture. Therefore, to address the gap between applications and FPGA/SoC architectures and to obtain an efficient hardware design for image analysis and highly demanding applications using the high-level synthesis tool, two complementary strategies are considered: ad-hoc techniques and performance estimator. Regarding ad-hoc techniques, three highly demanding applications were accelerated through HLS tools: pulse shape discriminator for cosmic rays, automatic pest classification, and re-ranking for information retrieval, emphasizing the benefits when this type of applications are traversed by compression techniques when targeting FPGA/SoC devices. Furthermore, a comprehensive performance estimator for hardware acceleration is proposed in this thesis to effectively predict the resource utilization and latency for FPGA/SoC, building a bridge between the application and architectural domains. The tool integrates analytical models for performance prediction, and a design space explorer (DSE) engine for providing high-level insights to hardware developers, composed of two independent sub-engines: DSE based on single-objective optimization and DSE based on evolutionary multi-objective optimization

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Disambiguation of Researcher Careers: Shifting the Perspective from Documents to Authors

The thesis describes an algorithm that disambiguates the namespaces of inventors and researchers, spawned by their patents and publications, into career paths. A probabilistic theory to assess the risk of erroneously linking documents of namesakes, different individuals with a mutual name, into one career bypasses the need for training datasets, thereby avoiding a namesake bias caused by the inherent underestimation of namesakes in training/benchmark data. The economic relevance of identified careers is illustrated by two applications. The first one outlines the impact of inter-regional inventor mobility in Italy on the total factor productivity of the sending and receiving regions. We show that an inflow of high skilled labor has a significant positive effect on TFP, while outflow decreases it. We further separate mobility in firm-internal relocation and job switches to find a more pronounced effect for the latter mobility. The second application observes the reaction of German university researchers to an exogenous change in federal law pertaining the property rights of their inventions equivalent to the Bayh Dole Act. Being able to trace their careers along with the careers of an unaffected control group allows us to evaluate the efficacy of technology transfer offices replacing the former informal activities of the university professors in regard of academic entrepreneurship. We find that an overall decrease of university patenting neutralizes any institutionalized efforts of spurring entrepreneurship at the expense of informal faculty-firm networks as channels for knowledge transfer

Social Network Analysis: A Machine Learning Approach

Social Network Analysis (SNA) is an appealing research topic, within the domain of Artificial Intelligence (AI), owing to its widespread application in the real world. In this dissertation, we have proposed effective Machine Learning (ML) and Deep Learning (DL) approaches toward resolving these open problems with regard to SNA, viz: Breakup Prediction, Link Prediction, Node Classification, Event-based Analysis, and Trend/Pattern Analysis. SNA can be employed toward resolving several real-world problems; and ML as well as DL have proven to be very effective methodologies for accomplishing Artificial Intelligence (AI)- related goals. Existing literature have focused on studying the apparent and latent interactions within social graphs as an n-ary operation, which yields binary outputs comprising positives (friends, likes, etc.) and negatives (foes, dislikes, etc.). Inasmuch as interactions constitute the bedrock of any given Social Network (SN) structure; there exist scenarios where an interaction, which was once considered a positive, transmutes into a negative as a result of one or more indicators which have affected the interaction quality. At present, this transmutation has to be manually executed by the affected actors in the SN. These manual transmutations can be quite inefficient, ineffective, and a mishap might have been incurred by the constituent actors and the SN structure prior to a resolution. Thus, as part of the research contributions of this dissertation, we have proposed an automatic technique toward flagging positive ties that should be considered for breakups or rifts (negative-tie state), as they tend to pose potential threats to actors and the SN. Furthermore, in this dissertation, we have proposed DL-based approaches based on edge sampling strategy for resolving the problems of Breakup Prediction, Link Prediction, and Node Classification. Also, we have proposed ML-based approaches for resolving the problems of Event-based Analysis and Trend/Pattern Analysis. We have evaluated our respective approaches against benchmark social graphs, and our results have been comparatively encouraging as documented herein