Multicore neuromorphic platforms come with a custom library for efficient development of neural n... more Multicore neuromorphic platforms come with a custom library for efficient development of neural network simulations. While these architectures are mainly focused on real-time biological network simulation using detailed neuron models, their application to a wider range of computational tasks is increasing. The reason is their effective support for parallel computation characterised by an intensive communication among processing nodes and their inherent energy efficiency. However, to unlock the full potential of these architectures for a wide range of applications, a library support for a more general computational model has to be developed. This work focuses on the implementation of a standard MPI interface for parallel programming of neuromorphic multicore architectures. The MPI library has been developed on top of the SpiNNaker multi-core neuromorphic platform, featuring a toroid interconnect and packet support for multicast communication. The proposed MPI implementation has been ...
To cope with the increasing complexity of digital systems programming, deep learning techniques h... more 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 characterised by increasing heterogeneity and parallelism, one of the most challenging and specific problems is efficiently allocating computational kernels to available hardware resources. In this field, deep learning applied to source code can be a key enabler to face this complexity. However, due to the rapid development of such techniques, it is not easy to understand which of those are suitable and most promising for this class of systems. For this purpose, we discuss recent developments in deep learning for source code analysis, and focus on techniques for kernel mapping on heterogeneous platforms, highlighting recent results, challenges an...
In this work, we present an innovative approach for damage detection of infrastructures on-edge d... more 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 enco...
Abstract—E-science applications involve great deal of data, to satisfy these processing requests,... more Abstract—E-science applications involve great deal of data, to satisfy these processing requests, distributed computing paradigms, such as cluster, Grid, Virtual Grid, Cloud Com-puting, and Hybrid System are growing exponentially. Existing computing infrastructures, software system design, and use cases have to take into account the enormity in volume of requests, size of data and computing load. In Bioinformatics field, such as in Next Generation Sequencing technology, in order to have more accurate analysis, it increases the amount of data to process. A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, produces millions of short sequence fragments in a single run. These fragments can be used to measure levels of gene expression and to identify novel splice variants of genes. The proposed solution allows to make the system scalable and flexible reducing elaboration time. The first aspect covers reverse engineering of a fast splice junction mapper for RNA-Se...
Controlled Natural Ventilation (CNV) is one of the potential most effective passive cooling techn... more Controlled Natural Ventilation (CNV) is one of the potential most effective passive cooling technique to reduce cooling needs of buildings in temperate-hot climate zones. However, a correct balance amid internal heat capacity, thermal insulation, and net opening area is important to achieve optimal results. The present paper shows results from an original simulation process carried out within the Course “ICT in building design” of the Master degree programme ICT4SS (ICT for smart societies) at the Politecnico di Torino. An office two-zone unit in Turin was simulated for fixed values of thermal insulation and internal heat capacity and increasing progressively external net opening area. Dynamic energy simulation was conducted for the cooling season period (May 1st ÷ September 30th) using Design Builder. The .idf output files were then used for the regression analysis, carried out by developing a Python script to allow for running the iterative process. A noise level related to a poss...
Proximity beacons are small, low-power devices capable of transmitting information at a limited d... more 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 configuratio...
Porto, the institutional repository of the Politecnico di Torino, is provided by the University L... more Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters.
SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architect... more 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 synchronis...
Counterbalancing climate change is one of the biggest challenges for engineers around the world. ... more 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 se...
Predicting power demand of building heating systems is a challenging task due to the high variabi... more 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 ...
In colorectal cancer (CRC), WNT pathway activation by genetic rearrangements of RSPO3 is emerging... more In colorectal cancer (CRC), WNT pathway activation by genetic rearrangements of RSPO3 is emerging as a promising target. However, its low prevalence severely limits availability of preclinical models for in-depth characterization. Using a pipeline designed to suppress stroma-derived signal, we find that RSPO3 "outlier" expression in CRC samples highlights translocation and fusion transcript expression. Outlier search in 151 CRC cell lines identified VACO6 and SNU1411 cells as carriers of, respectively, a canonical PTPRK(e1)-RSPO3(e2) fusion and a novel PTPRK(e13)-RSPO3(e2) fusion. Both lines displayed marked in vitro and in vivo sensitivity to WNT blockade by the porcupine inhibitor LGK974, associated with transcriptional and morphological evidence of WNT pathway suppression. Long-term treatment of VACO6 cells with LGK974 led to the emergence of a resistant population carrying two frameshift deletions of the WNT pathway inhibitor AXIN1, with consequent protein loss. Suppre...
Multicore neuromorphic platforms come with a custom library for efficient development of neural n... more Multicore neuromorphic platforms come with a custom library for efficient development of neural network simulations. While these architectures are mainly focused on real-time biological network simulation using detailed neuron models, their application to a wider range of computational tasks is increasing. The reason is their effective support for parallel computation characterised by an intensive communication among processing nodes and their inherent energy efficiency. However, to unlock the full potential of these architectures for a wide range of applications, a library support for a more general computational model has to be developed. This work focuses on the implementation of a standard MPI interface for parallel programming of neuromorphic multicore architectures. The MPI library has been developed on top of the SpiNNaker multi-core neuromorphic platform, featuring a toroid interconnect and packet support for multicast communication. The proposed MPI implementation has been ...
To cope with the increasing complexity of digital systems programming, deep learning techniques h... more 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 characterised by increasing heterogeneity and parallelism, one of the most challenging and specific problems is efficiently allocating computational kernels to available hardware resources. In this field, deep learning applied to source code can be a key enabler to face this complexity. However, due to the rapid development of such techniques, it is not easy to understand which of those are suitable and most promising for this class of systems. For this purpose, we discuss recent developments in deep learning for source code analysis, and focus on techniques for kernel mapping on heterogeneous platforms, highlighting recent results, challenges an...
In this work, we present an innovative approach for damage detection of infrastructures on-edge d... more 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 enco...
Abstract—E-science applications involve great deal of data, to satisfy these processing requests,... more Abstract—E-science applications involve great deal of data, to satisfy these processing requests, distributed computing paradigms, such as cluster, Grid, Virtual Grid, Cloud Com-puting, and Hybrid System are growing exponentially. Existing computing infrastructures, software system design, and use cases have to take into account the enormity in volume of requests, size of data and computing load. In Bioinformatics field, such as in Next Generation Sequencing technology, in order to have more accurate analysis, it increases the amount of data to process. A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, produces millions of short sequence fragments in a single run. These fragments can be used to measure levels of gene expression and to identify novel splice variants of genes. The proposed solution allows to make the system scalable and flexible reducing elaboration time. The first aspect covers reverse engineering of a fast splice junction mapper for RNA-Se...
Controlled Natural Ventilation (CNV) is one of the potential most effective passive cooling techn... more Controlled Natural Ventilation (CNV) is one of the potential most effective passive cooling technique to reduce cooling needs of buildings in temperate-hot climate zones. However, a correct balance amid internal heat capacity, thermal insulation, and net opening area is important to achieve optimal results. The present paper shows results from an original simulation process carried out within the Course “ICT in building design” of the Master degree programme ICT4SS (ICT for smart societies) at the Politecnico di Torino. An office two-zone unit in Turin was simulated for fixed values of thermal insulation and internal heat capacity and increasing progressively external net opening area. Dynamic energy simulation was conducted for the cooling season period (May 1st ÷ September 30th) using Design Builder. The .idf output files were then used for the regression analysis, carried out by developing a Python script to allow for running the iterative process. A noise level related to a poss...
Proximity beacons are small, low-power devices capable of transmitting information at a limited d... more 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 configuratio...
Porto, the institutional repository of the Politecnico di Torino, is provided by the University L... more Porto, the institutional repository of the Politecnico di Torino, is provided by the University Library and the IT-Services. The aim is to enable open access to all the world. Please share with us how this access benefits you. Your story matters.
SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architect... more 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 synchronis...
Counterbalancing climate change is one of the biggest challenges for engineers around the world. ... more 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 se...
Predicting power demand of building heating systems is a challenging task due to the high variabi... more 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 ...
In colorectal cancer (CRC), WNT pathway activation by genetic rearrangements of RSPO3 is emerging... more In colorectal cancer (CRC), WNT pathway activation by genetic rearrangements of RSPO3 is emerging as a promising target. However, its low prevalence severely limits availability of preclinical models for in-depth characterization. Using a pipeline designed to suppress stroma-derived signal, we find that RSPO3 "outlier" expression in CRC samples highlights translocation and fusion transcript expression. Outlier search in 151 CRC cell lines identified VACO6 and SNU1411 cells as carriers of, respectively, a canonical PTPRK(e1)-RSPO3(e2) fusion and a novel PTPRK(e13)-RSPO3(e2) fusion. Both lines displayed marked in vitro and in vivo sensitivity to WNT blockade by the porcupine inhibitor LGK974, associated with transcriptional and morphological evidence of WNT pathway suppression. Long-term treatment of VACO6 cells with LGK974 led to the emergence of a resistant population carrying two frameshift deletions of the WNT pathway inhibitor AXIN1, with consequent protein loss. Suppre...
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Papers by Andrea Acquaviva