STEFANO DI CARLO

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Associate Professor

+39 0110907080 / 7080 (DAUIN)

Research groups
Laboratories LAB 6 - Research laboratory
Research projects

Funded by competitive calls

  • Approximate Computing for Power and Energy Optimisation, (2020-2024) - Responsabile Scientifico

    UE-funded research - H2020 - Excellent Science - Marie Curie

    View project record on CORDIS

    ERC sectors

    PE6_1 - Computer architecture, pervasive computing, ubiquitous computing PE6_2 - Computer systems, parallel/distributed systems, sensor networks, embedded systems, cyber-physical systems PE6_11 - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)

    SDG

    Obiettivo 4. Fornire un’educazione di qualità, equa ed inclusiva, e opportunità di apprendimento per tutti Obiettivo 7. Assicurare a tutti l’accesso a sistemi di energia economici, affidabili, sostenibili e moderni

    Abstract

    The Approximate Computing for Power and Energy Optimisation ETN will train 15 ESRs to tackle the challenges of future embedded and high-performance computing energy efficiency by using disruptive methodologies. Following the current trend, by 2040 computers will need more electricity than the world energy resources can generate. On the communications side, energy consumption in mobile broadband networks is comparable to datacenters. To make things worse, Internet-of-Things will soon connect up to 50 billion devices through wireless networks to the cloud. APROPOS aims at decreasing energy consumption in both distributed computing and communications for cloud-based cyber-physical systems. We propose adaptive Approximate Computing to optimize energy-accuracy trade-offs. Luckily, in many parts of the global data acquisition, transfer, computation, and storage systems there exists the possibility to trade off accuracy to less power and less time consumed. As examples, numerous sensors are measuring noisy or inexact inputs; the algorithms processing the acquired signals can be stochastic; the applications using the data may be satisfied with an acceptable accuracy instead of exact and absolutely correct results; the system may be resilient against occasional errors; and a coarse classification may be enough for a data mining system. By introducing a new dimension, accuracy, to the design optimization, the energy efficiency can even be improved by a factor of 10x-50x. We will train the spearheads of the future generation to cope with the technologies, methodologies, and tools for successfully applying Approximate Computing to power and energy saving. The training, in this first ever ITN addressing approximate computing, is to a large extent done by researching energy-accuracy trade-offs on circuit, architecture, software, and system-level solutions, bringing together world leading experts from European organizations to train the ESR fellows.

    Countries

    • Austria
    • Svezia
    • Svizzera
    • Regno Unito
    • Finlandia
    • Italia
    • Francia
    • Spagna
    • Paesi Bassi

    Institutes/Companies

    • TECHNISCHE UNIVERSITAET WIEN
    • KUNGLIGA TEKNISKA HOEGSKOLAN
    • IBM RESEARCH GMBH
    • THE QUEEN'S UNIVERSITY OF BELFAST
    • WIREPAS OY
    • POLITECNICO DI TORINO
    • ECOLE CENTRALE DE LYON
    • POLITECNICO DI MILANO
    • ALMA MATER STUDIORUM - UNIVERSITA DI BOLOGNA
    • UNIVERSITAT POLITECNICA DE VALENCIA
    • TURUN YLIOPISTO
    • TECHNISCHE UNIVERSITEIT DELFT
    • UNIVERSITEIT VAN AMSTERDAM

    Departments

  • Metodo di simulazione via computer dell'ontogenesi di un sistema biologico e, opzionalmente, di generazione di un protocollo di coltura, (2020-2021) - Responsabile Scientifico

    Corporate-funded and donor-funded research

    ERC sectors

    PE6_13 - Bioinformatics, biocomputing, and DNA and molecular computation systems, cyber-physical systems

    SDG

    Obiettivo 3. Assicurare la salute e il benessere per tutti e per tutte le età

    Abstract

    I protocolli di coltura sono procedure che prevedono la somministrazione organizzata per fasi temporali di nutrienti e segnali al sistema biologico per implementare funzionalità desiderate. Spesso derivano dalla limitata conoscenza di un processo biologico, e vengono migliorati empiricamente. Questo è costoso in termini di tempo e risorse, poco riproducibile e tracciabile. L’ottimizzazione dei protocolli di coltura attualmente si concentra su funzionalità di base delle cellule (sopravvivenza, crescita, etc.), seguendo paradigmi di Design of Experiment (DoE): individua i valori ottimali di parametri di coltura noti per ottimizzare un singolo output specifico. In generale, questo approccio mira a rendere più efficaci protocolli esistenti. La soluzione proposta è un metodo computazionale per generare, a partire dalla conoscenza esistente sul sistema biologico e sul processo di coltura, protocolli di coltura nuovi, migliori e adatti a processi complessi come la biofabbricazione.

    Countries

    • ITALIA

    Departments

  • CLERECO-CROSS-LAYER EARLY RELIABILITY EVALUATION FOR THE COMPUTING CONTINUUM, (2013-2016) - Responsabile Scientifico

    UE-funded research - VII PQ - COOPERATION - ICT

    Countries

    • ITALIA

    Departments

Funded by commercial contracts

  • Procedure di collaudo funzionale da eseguire a run-time su processori ARM®, Intel e relative periferiche, (2018-2020) - Responsabile Scientifico

    Commercial Research

    Countries

    • ITALIA

    Institutes/Companies

    • RFI - RETE FERROVIARIA ITALIANA SPA

    Departments

  • Sistemi Embedded per applicazioni ferroviarie, (2018-2020) - Responsabile Scientifico

    Commercial Research

    Countries

    • ITALIA

    Institutes/Companies

    • RFI - RETE FERROVIARIA ITALIANA SPA

    Departments