Andrea Acquaviva

• PhD Computer Engineering
• Professor (Associate) at Polytechnic University of Turin

Spiking Neural Network (SNN) inference has a clear potential for high energy efficiency as computation is triggered by events. However, the inherent sparsity of events poses challenges for conventional computing systems, driving the development of specialized neuromorphic processors, which come with high silicon area costs and lack the flexibility...

Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving...

Power management (PM) is cumbersome for today’s computing systems. Attainable performance is bounded by the architecture’s computing efficiency and capped in temperature, current, and power. PM is composed of multiple interacting layers. High-level controllers (HLCs) involve application-level policies, operating system agents (OSPMs), and PM govern...

The rapid advancement and exploration of open-hardware RISC-V platforms are catalyzing substantial changes across critical sectors, including autonomous vehicles, smart-city infrastructure, and medical devices. Within this technological evolution, OpenTitan emerges as a groundbreaking open-source RISC-V design, renowned for its comprehensive securi...

LiDAR is the foundation of many autonomous vehicle perception systems, so it is essential to study and ensure the integrity and robustness of the data collected by LiDAR. To facilitate future research into robust and resilient LiDAR processing, we present a dataset containing a collection of uncontaminated and realistically contaminated LiDAR sampl...

In this work, we target the efficient implementation of spiking neural networks (SNNs) for low-power and low-latency applications. In particular, we propose a methodology for tuning SNN spiking activity with the objective of reducing computation cycles and energy consumption. We performed an analysis to devise key hyper-parameters, and then we show...

The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical devices. OpenTitan stands out as a groundbreaking open-source RISC-V design with a comprehensive security toolkit as a standalone system-on-chip (SoC). OpenTitan includes...

The number of applications of structural health monitoring techniques to buildings is steadily increasing, fostered by the diffusion of low-cost accelerometric sensors based on MEMS. In many cases, these sensors may represent an alternative to more expensive transducers, such as piezoelectric accelerometers, even if they typically have lower signal...

Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving...

In this work, we present a novel distributed software platform for patients with neurodegenerative diseases that affect motor neurons. The linking point between this wide range of diseases is the strong social impact they have, degrading the freedom of action of the patient; the loss of functionality of motor neurons, caused by progressive degradat...

Modern space applications impose significant challenges to the design of hardware and software platforms. Beyond traditional applications such as avionics, Attitude Orbit Control, and signal/telemetry processing, new developments increasingly leverage Machine Learning models to enhance the autonomy of spacecraft. Such AI-based functionalities promi...

Workplace safety is a prominent concern, motivating researchers across diverse disciplines to investigate valuable ways to address its challenges. However, creating an efficient system to address this issue remains a significant challenge. Since many accidents happen due to improper usage or complete removal of Personal Protective Equipment (PPE),...

The structural performance assessment of bridges is a crucial issue for managing transportation infrastructure systems in EU countries as traffic loads and structural ageing continues to increase. Weight-in-Motion (WiM) systems have been developed to estimate the gross weight of vehicles over a bridge and keep the bridge load under control. However...

Today, significant advances in science and technology can not be envisioned without high computing capacity. To solve large problems in science, engineering, and business, data centers provide High-Performance Computing (HPC) systems with aggregation of the computing capacity of thousand of computing nodes with the cost of millions of euros per yea...

Reinforcement Learning (RL) is widely used for training Unmanned Aerial Vehicles (UAVs) involving complex perception information (e.g., camera, lidar). RL achievable performance is affected by the time needed to learn from the direct interaction of the agent with the environment. AirSim is a widely used simulator for autonomous UAV research, and it...

This chapter presents a novel distributed software infrastructure to enable energy management and simulation of novel control strategies in smart cities. In this context, the following heterogeneous information, describing the different entities in a city, needs to be taken into account to form a unified district information model: internet-of-thin...

Monitoring applications are increasingly important to enable predictive maintenance and real-time anomaly detection in industrial and civil safety-critical infrastructures. Typical monitoring pipelines consist of a sensor network that collects and streams IoT data toward a cloud infrastructure that provides storage, visualisation and data analytic...

To cope with the increasing complexity of digital systems programming, deep learning techniques have recently been proposed to enhance software deployment by analysing source code for different purposes, ranging from performance and energy improvement to debugging and security assessment. As embedded platforms for cyber-physical systems are charact...

The new open and royalty-free RISC-V ISA is attracting interest across the whole computing continuum, from microcontrollers to supercomputers. High-performance RISC-V processors and accelerators have been announced, but RISC-V-based HPC systems will need a holistic co-design effort, spanning memory, storage hierarchy interconnects and full software...

Despite its relatively recent history, Deep Learning (DL) based source code analysis is already a cornerstone in machine learning for compiler optimization. When applied to the classification of pieces of code to identify the best computation unit in a heterogeneous Systems-on-Chip, it can be effective in supporting decisions that a programmer has...

Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining solar energy production of photovoltaic systems, represents an attractive topic nowadays. Solar irradia...

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 l...

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...

In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the n...

Datacenters play a vital role in today's society. At large, a datacenter room is a complex controlled environment composed of thousands of computing nodes, which consume kW of power. To dissipate the power, forced air/liquid flow is employed, with a cost of millions of euros per year. Reducing this cost involves using free-cooling and average case...

The application of Artificial Intelligence is becoming common in many engineering fields. Among them, one of the newest and rapidly evolving is software generation, where AI can be used to automatically optimise the implementation of an algorithm for a given computing platform. In particular, Deep Learning technologies can be used to the decide how...

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the nu...

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule-based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of h...

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 t...

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 indoo...

This paper focuses on an algorithmic approach to building design, based on the environmental and technological vision to the performance-driven design methodology. The performed analysis concerns to the development of an algorithm with the aim of optimizing, through dynamic energy simulation, the energy and technological definition of a building en...

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 transm...

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 produce...

Nowadays, green energy is considered as a viable solution to hinder \( CO_{2} \) emissions and greenhouse effects. Indeed, it is expected that Renewable Energy Sources (RES) will cover \( 40\% \) of the total energy request by 2040. This will move forward decentralized and cooperative power distribution systems also called smart grids. Among RES, s...

In this paper, we propose a methodology for efficiently mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a Spiking Neural Network (SNN) in real-time. The problem of neuron-to-core mapping is relevant as a non-efficient allocation may impact real-time and reliabil...

Modern heterogeneous platforms require compilers capable of choosing the appropriate device for the execution of program portions. This paper presents a machine learning method designed for supporting mapping decisions through the analysis of the program source code represented in LLVM assembly language (IR) for exploiting the advantages offered by...

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/...

Continuous Glucose Monitoring Systems (CGMSs) allow measuring the blood glycaemic value of a diabetic patient at a high sampling rate, producing considerable amount of data. This data can be effectively used by machine learning techniques to infer future values of the glycaemic concentration, allowing the early prevention of dangerous hyperglycaemi...

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-re...

Neuromorphic architectures are emerging not only for real-time simulation of brain-scale biological neural networks but also to support innovative brain-inspired computational paradigms. In both domains there is an increasing demand for flexibility in terms of network configuration and runtime redesign of network parameters and simulated neurons mo...

The continuous evolution of internet of things technologies is constantly evolving the concept of smart cities as well as the surrounding environments. From pervasive sensors through computational nodes at the edge of the network to the cloud and final user applications, the data flow chain makes available to the user a very large and heterogeneous...

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 i...

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multiscale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and nat...

For planning and development and in real-time operation of smart grids, it is important to evaluate the impacts of photovoltaic (PV) distributed generation. This paper presents an integrated platform, constituted of two main components: a PV simulator and a real-time distribution network simulator. The first, designed and developed following the mi...

Novel Information and Communication Technologies, such as Internet-of-Things (IoT), middleware and cloud computing, are providing innovative solutions ranging in different contexts. Smart health is one of these scenarios. Indeed, there is a rising interest in developing new healthcare services for remote patient assistance and monitoring. Among all...

The growing global urbanization rate implies that the sustainability challenges are increasingly concentrated in cities. At today, around 75% of global energy is consumed in urban areas, so efforts must be addressed to transform existing urban energy systems into more sustainable systems. In this perspective, a key aspect to evolve toward a cleaner...

In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of CO2 pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be dev...

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 p...

Urban districts should evolve towards a more sustainable infrastructure and greener energy carriers. The utmost challenge is the smart integration and control, within the existing infrastructure, of new information and energy technologies (such as sensors, appliances, electric and thermal power and storage devices) that are able to provide multi-se...

Data management has been one of the most interesting research fields within the smart city framework over the last years, with the aim of optimizing energy saving at district level. This topic involves the creation of a 3D city model considering heterogeneous datasets, such as Building Information Models (BIMs), Geographical Information Systems (GI...

Nowadays, we are moving forward to more sustainable societies, where a crucial issue consists in reducing footprint and greenhouse emissions. This transition can be achieved by increasing the penetration of distributed renewable energy sources together with a smarter use of energy. To achieve it, new tools are needed to plan the deployment of such...

In recent years, the research about energy waste and CO2 emission reduction has gained a strong momentum, also pushed by European and national funding initiatives. The main purpose of this large effort is to reduce the effects of greenhouse emission, climate change to head for a sustainable society. In this scenario, Information and Communication T...

Following the Smart City views, citizens, policy makers and energy distribution companies need a reliable and scalable infrastructure to manage and analyse energy consumption data in a city district context. In order to move forward this view, a city district model is needed, which takes into account dierent data-sources such as Building Informatio...

Smart Building is recent interdisciplinary research field that aims to improve the monitoring, management and maintenance of buildings. In this scenario, we present an innovative solution for combining BIM (Building Information Modelling) data with ambient information collected by heterogeneous devices deployed in the building. In order to collect...

Due to the increasing penetration of distributed generation, storage, electric vehicles and new ICT technologies, distribution networks are evolving towards the Smart Grid paradigm. For this reason, new control strategies, algorithms and technologies need to be tested and validated before their actual field implementation. In this paper we present...

Planning and developing the future Smart City is becoming mandatory due to the need of moving forward to a more sustainable society. To foster this transition an accurate simulation of energy production from renewable sources, such as Photovoltaic Panels (PV), is necessary to evaluate the impact on the grid. In this paper, we present a distributed...

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