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Prof. Andrea Acquaviva
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, 33 - 40126 Bologna, Italy

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Research Keywords & Expertise

0 Bioinformatics
0 Embedded Systems
0 low power
0 SoC
0 Multicore

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low power
Multicore
Embedded Systems
SoC
Bioinformatics

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Short Biography

Andrea Acquaviva is Full Professor at the Department of Electric, Electronic and Information Engineering (DEI) at University of Bologna. He got a Ph.D. degree in electrical engineering from Bologna University, Italy and in Physics of Complex Systems from University of Turin. He has been research intern at Hewlett Packard Labs in Palo Alto, CA, USA and visiting researcher at the Laboratoire de Systemes Integrés at EPFL, CH. His research interests focus on: Programming models, compilers and runtime for low-power embedded systems, multicore and heterogeneous HPC and embedded platforms, IoT and edge computing, cyber-physical systems, digital twins of CPS, neuromorphic computing. In the fields above, he has authored over 200 scientific publications.

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Journal article
Published: 23 August 2021 in Future Internet
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In this work, we present an innovative approach for damage detection of infrastructures on-edge devices, exploiting a brain-inspired algorithm. The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their theoretical energy efficiency and compactness, to recognise damage conditions by processing data from low-cost accelerometers (MEMS) directly on the sensor node. We focus on designing an efficient coding of MEMS data to optimise SNN execution on a low-power microcontroller. We characterised and profiled LSNN performance and energy consumption on a hardware prototype sensor node equipped with an STM32 embedded microcontroller and a digital MEMS accelerometer. We used a hardware-in-the-loop environment with virtual sensors generating data on an SPI interface connected to the physical microcontroller to evaluate the system with a data stream from a real viaduct. We exploited this environment also to study the impact of different on-sensor encoding techniques, mimicking a bio-inspired sensor able to generate events instead of accelerations. Obtained results show that the proposed optimised embedded LSNN (eLSNN), when using a spike-based input encoding technique, achieves 54% lower execution time with respect to a naive LSNN algorithm implementation present in the state-of-the-art. The optimised eLSNN requires around 47 kCycles, which is comparable with the data transfer cost from the SPI interface. However, the spike-based encoding technique requires considerably larger input vectors to get the same classification accuracy, resulting in a longer pre-processing and sensor access time. Overall the event-based encoding techniques leads to a longer execution time (1.49×) but similar energy consumption. Moving this coding on the sensor can remove this limitation leading to an overall more energy-efficient monitoring system.

ACS Style

Francesco Barchi; Luca Zanatta; Emanuele Parisi; Alessio Burrello; Davide Brunelli; Andrea Bartolini; Andrea Acquaviva. Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring. Future Internet 2021, 13, 219 .

AMA Style

Francesco Barchi, Luca Zanatta, Emanuele Parisi, Alessio Burrello, Davide Brunelli, Andrea Bartolini, Andrea Acquaviva. Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring. Future Internet. 2021; 13 (8):219.

Chicago/Turabian Style

Francesco Barchi; Luca Zanatta; Emanuele Parisi; Alessio Burrello; Davide Brunelli; Andrea Bartolini; Andrea Acquaviva. 2021. "Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring." Future Internet 13, no. 8: 219.

Preprint content
Published: 28 May 2021
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Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline’s implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.

ACS Style

Enrico Tabanelli; Davide Brunelli; Luca Benini; Andrea Acquaviva. TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH. 2021, 1 .

AMA Style

Enrico Tabanelli, Davide Brunelli, Luca Benini, Andrea Acquaviva. TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH. . 2021; ():1.

Chicago/Turabian Style

Enrico Tabanelli; Davide Brunelli; Luca Benini; Andrea Acquaviva. 2021. "TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH." , no. : 1.

Preprint content
Published: 28 May 2021
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Non-Intrusive Load Monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost, resource-constrained MCU-based meters is currently an open challenge. This paper addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing State-of-the-Art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline’s implementation on a MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most-accurate feature vector deployment (96.19%) while achieving up to 5.45x speed-up and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware design.

ACS Style

Enrico Tabanelli; Davide Brunelli; Luca Benini; Andrea Acquaviva. TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH. 2021, 1 .

AMA Style

Enrico Tabanelli, Davide Brunelli, Luca Benini, Andrea Acquaviva. TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH. . 2021; ():1.

Chicago/Turabian Style

Enrico Tabanelli; Davide Brunelli; Luca Benini; Andrea Acquaviva. 2021. "TRIMMING FEATURE EXTRACTION AND INFERENCE FOR MCU-BASED EDGE NILM: A SYSTEMATIC APPROACH." , no. : 1.

Journal article
Published: 11 May 2021 in Expert Systems with Applications
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Nowadays, we are moving forward to more sustainable energy production systems based on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In this view, new models are needed to forecast Global Horizontal Solar Irradiance (GHI), which strongly influences PV production. For example, this forecast is crucial to develop novel control strategies for smart grid management. In this paper, we present a novel methodology to forecast GHI in short- and long-term time-horizons, i.e. from next 15 min up to next 24 h. It implements machine learning techniques to achieve this purpose. We start from the analysis of a real-world dataset with different meteorological information including GHI, in the form of time-series. Then, we combined Variational Mode Decomposition (VMD) and two Convolutional Neural Networks (CNN) together with Random Forest (RF) or Long Short Term Memory (LSTM). Finally, we present the experimental results and discuss their accuracy.

ACS Style

Davide Cannizzaro; Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Andrea Acquaviva; Edoardo Patti. Solar radiation forecasting based on convolutional neural network and ensemble learning. Expert Systems with Applications 2021, 181, 115167 .

AMA Style

Davide Cannizzaro, Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Andrea Acquaviva, Edoardo Patti. Solar radiation forecasting based on convolutional neural network and ensemble learning. Expert Systems with Applications. 2021; 181 ():115167.

Chicago/Turabian Style

Davide Cannizzaro; Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Andrea Acquaviva; Edoardo Patti. 2021. "Solar radiation forecasting based on convolutional neural network and ensemble learning." Expert Systems with Applications 181, no. : 115167.

Journal article
Published: 22 April 2020 in Energies
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Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.

ACS Style

Marco Massano; Edoardo Patti; Enrico Macii; Andrea Acquaviva; Lorenzo Bottaccioli. An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings. Energies 2020, 13, 2097 .

AMA Style

Marco Massano, Edoardo Patti, Enrico Macii, Andrea Acquaviva, Lorenzo Bottaccioli. An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings. Energies. 2020; 13 (8):2097.

Chicago/Turabian Style

Marco Massano; Edoardo Patti; Enrico Macii; Andrea Acquaviva; Lorenzo Bottaccioli. 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings." Energies 13, no. 8: 2097.

Journal article
Published: 28 December 2019 in Electronics
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Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters.

ACS Style

Davide Cannizzaro; Marina Zafiri; Daniele Jahier Pagliari; Edoardo Patti; Enrico Macii; Massimo Poncino; Andrea Acquaviva. A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments. Electronics 2019, 9, 44 .

AMA Style

Davide Cannizzaro, Marina Zafiri, Daniele Jahier Pagliari, Edoardo Patti, Enrico Macii, Massimo Poncino, Andrea Acquaviva. A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments. Electronics. 2019; 9 (1):44.

Chicago/Turabian Style

Davide Cannizzaro; Marina Zafiri; Daniele Jahier Pagliari; Edoardo Patti; Enrico Macii; Massimo Poncino; Andrea Acquaviva. 2019. "A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments." Electronics 9, no. 1: 44.

Journal article
Published: 14 November 2019 in Electronics
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SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform.

ACS Style

Gianvito Urgese; Francesco Barchi; Emanuele Parisi; Evelina Forno; Andrea Acquaviva; Enrico Macii. Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm. Electronics 2019, 8, 1342 .

AMA Style

Gianvito Urgese, Francesco Barchi, Emanuele Parisi, Evelina Forno, Andrea Acquaviva, Enrico Macii. Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm. Electronics. 2019; 8 (11):1342.

Chicago/Turabian Style

Gianvito Urgese; Francesco Barchi; Emanuele Parisi; Evelina Forno; Andrea Acquaviva; Enrico Macii. 2019. "Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm." Electronics 8, no. 11: 1342.

Journal article
Published: 02 September 2019 in Electronics
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In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models.

ACS Style

Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings. Electronics 2019, 8, 979 .

AMA Style

Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Santa Di Cataldo, Andrea Acquaviva, Edoardo Patti. A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings. Electronics. 2019; 8 (9):979.

Chicago/Turabian Style

Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. 2019. "A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings." Electronics 8, no. 9: 979.

Journal article
Published: 01 August 2019 in Proceedings of the Institution of Civil Engineers - Engineering Sustainability
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For planning and development and in real-time operation of smart grids, it is important to evaluate the impacts of photovoltaic (PV) distributed generation. In this paper, we present an integrated platform, constituted by two main components: a PV simulator and a real-time distribution network simulator. The first, designed and developed following the microservice approach and providing REST web services, simulates real-sky solar radiation on rooftops and estimates the PV energy production; the second, based on a digital real-time power systems simulator, simulates the behaviour of the electric network under the simulated generation scenarios. The platform is tested on a case study based on real data for a district of the city of Turin, Italy. In the results, we show possible applications of the platform for power flow forecasting during real-time operation and to detect possible voltage and transformers capacity problems during planning due to high penetration of Renewable Energy Sources. In particular, the results show that the case study distribution network, in the actual configuration, is not ready to accommodate all the generation capacity that can be installed as, in certain hours of the day and in certain days of the year, the capacity of some transformers is exceeded.

ACS Style

Lorenzo Bottaccioli; Abouzar Estebsari; Edoardo Patti; Enrico Pons; Andrea Acquaviva. Planning and real-time management of smart grids with high PV penetration in Italy. Proceedings of the Institution of Civil Engineers - Engineering Sustainability 2019, 172, 272 -282.

AMA Style

Lorenzo Bottaccioli, Abouzar Estebsari, Edoardo Patti, Enrico Pons, Andrea Acquaviva. Planning and real-time management of smart grids with high PV penetration in Italy. Proceedings of the Institution of Civil Engineers - Engineering Sustainability. 2019; 172 (6):272-282.

Chicago/Turabian Style

Lorenzo Bottaccioli; Abouzar Estebsari; Edoardo Patti; Enrico Pons; Andrea Acquaviva. 2019. "Planning and real-time management of smart grids with high PV penetration in Italy." Proceedings of the Institution of Civil Engineers - Engineering Sustainability 172, no. 6: 272-282.

Journal article
Published: 31 May 2019 in Sustainability
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Counterbalancing climate change is one of the biggest challenges for engineers around the world. One of the areas in which optimization techniques can be used to reduce energy needs, and with that the pollution derived from its production, is building design. With this study of a generic office located both in a northern country and in a temperate/Mediterranean site, we want to introduce a coding approach to dynamic energy simulation, able to suggest, from the early-design phases when the main building forms are defined, optimal configurations considering the energy needs for heating, cooling and lighting. Generally, early-design considerations of energy need reduction focus on the winter season only, in line with the current regulations; nevertheless a more holistic approach is needed to include other high consumption voices, e.g., for space cooling and lighting. The main considered design parameter is the WWR (window-to-wall ratio), even if further variables are considered in a set of parallel analyses (level of insulation, orientation, activation of low-cooling strategies including shading devices and ventilative cooling). Finally, the effect of different levels of occupancy was included in the analysis to regress results and compare the WWR with corresponding heating and cooling needs. This approach is adapted to Passivhaus design optimization, working on energy need minimisation acting on envelope design choices. The results demonstrate that it is essential to include, from the early-design configurations, a larger set of variables in order to optimize the expected energy needs on the basis of different aspects (cooling, heating, lighting, design choices). Coding is performed using Python scripting, while dynamic energy simulations are based on EnergyPlus.

ACS Style

Giacomo Chiesa; Andrea Acquaviva; Mario Grosso; Lorenzo Bottaccioli; Maurizio Floridia; Edoardo Pristeri; Edoardo Sanna. Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation. Sustainability 2019, 11, 3078 .

AMA Style

Giacomo Chiesa, Andrea Acquaviva, Mario Grosso, Lorenzo Bottaccioli, Maurizio Floridia, Edoardo Pristeri, Edoardo Sanna. Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation. Sustainability. 2019; 11 (11):3078.

Chicago/Turabian Style

Giacomo Chiesa; Andrea Acquaviva; Mario Grosso; Lorenzo Bottaccioli; Maurizio Floridia; Edoardo Pristeri; Edoardo Sanna. 2019. "Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation." Sustainability 11, no. 11: 3078.

Journal article
Published: 30 April 2019 in Electronics
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Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.

ACS Style

Andrea Acquaviva; Daniele Apiletti; Antonio Attanasio; Elena Baralis; Lorenzo Bottaccioli; Tania Cerquitelli; Silvia Chiusano; Enrico Macii; Edoardo Patti. Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine. Electronics 2019, 8, 491 .

AMA Style

Andrea Acquaviva, Daniele Apiletti, Antonio Attanasio, Elena Baralis, Lorenzo Bottaccioli, Tania Cerquitelli, Silvia Chiusano, Enrico Macii, Edoardo Patti. Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine. Electronics. 2019; 8 (5):491.

Chicago/Turabian Style

Andrea Acquaviva; Daniele Apiletti; Antonio Attanasio; Elena Baralis; Lorenzo Bottaccioli; Tania Cerquitelli; Silvia Chiusano; Enrico Macii; Edoardo Patti. 2019. "Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine." Electronics 8, no. 5: 491.

Journal article
Published: 29 March 2019 in IEEE Transactions on Emerging Topics in Computing
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ACS Style

Francesco Barchi; Gianvito Urgese; Alessandro Siino; Santa Di Cataldo; Enrico Macii; Andrea Acquaviva. Flexible On-Line Reconfiguration of Multi-Core Neuromorphic Platforms. IEEE Transactions on Emerging Topics in Computing 2019, 9, 915 -927.

AMA Style

Francesco Barchi, Gianvito Urgese, Alessandro Siino, Santa Di Cataldo, Enrico Macii, Andrea Acquaviva. Flexible On-Line Reconfiguration of Multi-Core Neuromorphic Platforms. IEEE Transactions on Emerging Topics in Computing. 2019; 9 (2):915-927.

Chicago/Turabian Style

Francesco Barchi; Gianvito Urgese; Alessandro Siino; Santa Di Cataldo; Enrico Macii; Andrea Acquaviva. 2019. "Flexible On-Line Reconfiguration of Multi-Core Neuromorphic Platforms." IEEE Transactions on Emerging Topics in Computing 9, no. 2: 915-927.

Chapter
Published: 01 January 2019 in Architecture and Design
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This chapter presents a methodology based on Building Information Modelling (BIM) and interoperability to convert existing buildings, even historical, into smart buildings. The chapter starts describing the main concepts of BIM and interoperability in the Architecture, Engineer and Construction (AEC) industry with special attention on integrating information from heterogeneous devices deployed in the building. Then, it details the SEEMPubS (Smart Energy Efficient Middleware for Public Buildings) middleware, which consists on three layers: (i) Integration Layer, (ii) Middleware Layer, and (iii) Application Layer. The validation of the most significant results is presented using both gamification and technical approaches involving different end-users. Finally, Apps for data management are introduced with a Community Portal and an Android Application for real-time data visualization. Future works introduce the integration of smart building into smart district context.

ACS Style

Anna Osello; Andrea Acquaviva; Daniele Dalmasso; David Erba; Matteo Del Giudice; Enrico Macii; Edoardo Patti. BIM and Interoperability for Cultural Heritage Through ICT. Architecture and Design 2019, 93 -111.

AMA Style

Anna Osello, Andrea Acquaviva, Daniele Dalmasso, David Erba, Matteo Del Giudice, Enrico Macii, Edoardo Patti. BIM and Interoperability for Cultural Heritage Through ICT. Architecture and Design. 2019; ():93-111.

Chicago/Turabian Style

Anna Osello; Andrea Acquaviva; Daniele Dalmasso; David Erba; Matteo Del Giudice; Enrico Macii; Edoardo Patti. 2019. "BIM and Interoperability for Cultural Heritage Through ICT." Architecture and Design , no. : 93-111.

Journal article
Published: 12 December 2018 in IEEE Access
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ACS Style

Lorenzo Bottaccioli; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. Realistic Multi-Scale Modeling of Household Electricity Behaviors. IEEE Access 2018, 7, 2467 -2489.

AMA Style

Lorenzo Bottaccioli, Santa Di Cataldo, Andrea Acquaviva, Edoardo Patti. Realistic Multi-Scale Modeling of Household Electricity Behaviors. IEEE Access. 2018; 7 ():2467-2489.

Chicago/Turabian Style

Lorenzo Bottaccioli; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. 2018. "Realistic Multi-Scale Modeling of Household Electricity Behaviors." IEEE Access 7, no. : 2467-2489.

Journal article
Published: 03 September 2018 in IEEE Transactions on Emerging Topics in Computing
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The seven papers in this special section focus on memory-based computing and 3D stacked hybrid memory architectures, on many-core neuromorphic platforms and clustered many-core accelerators, on emerging technologies for on-chip interconnects and signal reconstruction algorithms running on a heterogeneous mobile SoC and on the efficient parallelization of GPU primitives.

ACS Style

Alberto Macii; Andrea Acquaviva. Guest Editorial for the Special Section on Emerging Computational Paradigms. IEEE Transactions on Emerging Topics in Computing 2018, 6, 303 -304.

AMA Style

Alberto Macii, Andrea Acquaviva. Guest Editorial for the Special Section on Emerging Computational Paradigms. IEEE Transactions on Emerging Topics in Computing. 2018; 6 (3):303-304.

Chicago/Turabian Style

Alberto Macii; Andrea Acquaviva. 2018. "Guest Editorial for the Special Section on Emerging Computational Paradigms." IEEE Transactions on Emerging Topics in Computing 6, no. 3: 303-304.

Conference paper
Published: 01 September 2018 in 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)
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In this paper, we evaluate a partitioning and placement technique for mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. We designed a task placement pipeline capable of analysing the network of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuronto-core mapping problem has been formalised as a two phases problem: Partitioning and Placement. The Partitioning phase aims at grouping together the most connected network components, maximising the amount of self-connections within each identified group. For this purpose we used a multilevel k-way graph partitioning strategy capable of generating network-partitions. The Placement phase aims at placing groups of neurons over the chip mesh minimising the communication between computational nodes. For implementing this step, we designed and evaluate the performances of three placement variants. In the results, we point out the importance of using a partitioning algorithm for the SNN graph. We were able to achieve an increase in self-connections of 19% and an improvement of the final overall post-placement synaptic elongation of 29% using the simulated annealing placement technique, compared to 22% obtained without partitioning.

ACS Style

Francesco Barchi; Gianvito Urgese; Enrico Macii; Andrea Acquaviva. Work-in-Progress: Impact of Graph Partitioning on SNN Placement for a Multi-Core Neuromorphic Architecture. 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES) 2018, 1 -2.

AMA Style

Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva. Work-in-Progress: Impact of Graph Partitioning on SNN Placement for a Multi-Core Neuromorphic Architecture. 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES). 2018; ():1-2.

Chicago/Turabian Style

Francesco Barchi; Gianvito Urgese; Enrico Macii; Andrea Acquaviva. 2018. "Work-in-Progress: Impact of Graph Partitioning on SNN Placement for a Multi-Core Neuromorphic Architecture." 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES) , no. : 1-2.

Conference paper
Published: 01 September 2018 in 2018 International Conference on Smart Energy Systems and Technologies (SEST)
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In this paper, we present a novel distributed software infrastructure to foster new services in smart grids with particular emphasis on supporting self-healing distribution systems. This infrastructure exploits the rising Internet-of-Things paradigms to build and manage an interoperable peer-to-peer network of our prototype smart meters, also presented in this paper. The proposed three-phase smart meter, called 3-SMA, is a low cost and open-source Internet-connected device that provides features for self-configuration. In addition, it selectively run on-board-algorithms for smart grid management depending on its deployment on the distribution network. Finally, we present the experimental results of Hardware-In-the-Loop simulations we performed.

ACS Style

Abouzar Estebsari; Matteo Orlando; Enrico Pons; Andrea Acquaviva; Edoardo Patti. A Novel Internet-of-Things Infrastructure to Support Self-Healing Distribution Systems. 2018 International Conference on Smart Energy Systems and Technologies (SEST) 2018, 1 -6.

AMA Style

Abouzar Estebsari, Matteo Orlando, Enrico Pons, Andrea Acquaviva, Edoardo Patti. A Novel Internet-of-Things Infrastructure to Support Self-Healing Distribution Systems. 2018 International Conference on Smart Energy Systems and Technologies (SEST). 2018; ():1-6.

Chicago/Turabian Style

Abouzar Estebsari; Matteo Orlando; Enrico Pons; Andrea Acquaviva; Edoardo Patti. 2018. "A Novel Internet-of-Things Infrastructure to Support Self-Healing Distribution Systems." 2018 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.

Conference paper
Published: 01 September 2018 in 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES)
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In this era, the requirement of high-performance computing at low power cost can be met by the parallel execution of an application on a large number of programmable cores. Emerging many-core architectures provide dense interconnection fabrics leading to new communication requirements. In particular, the effective exploitation of synchronous and asynchronous channels for fast communication from/to internal cores and external devices is a key issue for these architectures. In this paper, we propose a methodology for clustering sequential commands used for configuring the parallel execution of tasks on a globally asynchronous locally synchronous multi-chip many-core neuromorphic platform. With the purpose of reducing communication costs and maximise the exploitation of the available communication bandwidth, we adapted the Multiple Sequence Alignment (MSA) algorithm for clustering the unicast streams of packets used for the configuration of each core so as to generate a coherent multicast stream that configures all cores at once. In preliminary experiments, we demonstrate how the proposed method can lead up to a 97% reduction in packet transmission thus positively affecting the overall communication cost.

ACS Style

Gianvito Urgese; Luca Peres; Francesco Barchi; Enrico Macii; Andrea Acquaviva. Work-in-Progress: Multiple Alignment of Packet Sequences for Efficient Communication in a Many-Core Neuromorphic System. 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES) 2018, 1 -2.

AMA Style

Gianvito Urgese, Luca Peres, Francesco Barchi, Enrico Macii, Andrea Acquaviva. Work-in-Progress: Multiple Alignment of Packet Sequences for Efficient Communication in a Many-Core Neuromorphic System. 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES). 2018; ():1-2.

Chicago/Turabian Style

Gianvito Urgese; Luca Peres; Francesco Barchi; Enrico Macii; Andrea Acquaviva. 2018. "Work-in-Progress: Multiple Alignment of Packet Sequences for Efficient Communication in a Many-Core Neuromorphic System." 2018 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES) , no. : 1-2.

Proceedings article
Published: 19 June 2018 in Air Pollution XXVI
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ACS Style

Marco Ravina; Edoardo Patti; Lorenzo Bottaccioli; Deborah Panepinto; Andrea Acquaviva; Maria Chiara Zanetti. IMPLEMENTING AIR-POLLUTION AND HEALTH-DAMAGE COSTS IN URBAN MULTI-ENERGY SYSTEMS MODELLING. Air Pollution XXVI 2018, 230, 95 -106.

AMA Style

Marco Ravina, Edoardo Patti, Lorenzo Bottaccioli, Deborah Panepinto, Andrea Acquaviva, Maria Chiara Zanetti. IMPLEMENTING AIR-POLLUTION AND HEALTH-DAMAGE COSTS IN URBAN MULTI-ENERGY SYSTEMS MODELLING. Air Pollution XXVI. 2018; 230 ():95-106.

Chicago/Turabian Style

Marco Ravina; Edoardo Patti; Lorenzo Bottaccioli; Deborah Panepinto; Andrea Acquaviva; Maria Chiara Zanetti. 2018. "IMPLEMENTING AIR-POLLUTION AND HEALTH-DAMAGE COSTS IN URBAN MULTI-ENERGY SYSTEMS MODELLING." Air Pollution XXVI 230, no. : 95-106.

Journal article
Published: 13 April 2018 in IEEE Transactions on Smart Grid
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In this work, we address the problem of providing fast and on-line households appliance load detection in a non-intrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours. To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches.

ACS Style

Million Abayneh Mengistu; Awet Abraha Girmay; Chirstian Camarda; Andrea Acquaviva; Edoardo Patti. A Cloud-Based On-Line Disaggregation Algorithm for Home Appliance Loads. IEEE Transactions on Smart Grid 2018, 10, 3430 -3439.

AMA Style

Million Abayneh Mengistu, Awet Abraha Girmay, Chirstian Camarda, Andrea Acquaviva, Edoardo Patti. A Cloud-Based On-Line Disaggregation Algorithm for Home Appliance Loads. IEEE Transactions on Smart Grid. 2018; 10 (3):3430-3439.

Chicago/Turabian Style

Million Abayneh Mengistu; Awet Abraha Girmay; Chirstian Camarda; Andrea Acquaviva; Edoardo Patti. 2018. "A Cloud-Based On-Line Disaggregation Algorithm for Home Appliance Loads." IEEE Transactions on Smart Grid 10, no. 3: 3430-3439.