TreeHouse, nourish your community

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- Silvia Brembati, Product Designer - Benedetta Bolis, Engineering Physics Student Due to the recent COVID-19 outbreak, everybody had to quickly rearrange their lifestyle and learn how to get through isolation. Keeping in touch has never been more compelling and challenging at the same time. A recent survey conducted in Italy, states that 80% of the population felt like they needed psychological support to get through quarantine. We believe that if people had a way to feel surrounded by their friends and had been able to share activities, this number would be significantly lower. This is where our new app TreeHouse comes in handy as it guides the user in contributing to the life of the community: a virtual tree will come to life and thrive thanks to both real-life and online interactions. Sharing content, chatting with friends, or drinking a cup of tea together will make a leaf or a branch grow, but if the user is missing for too long, the tree will suffer from their absence, in complete symbiosis. Nevertheless, checking how the tree develops helps the members feel the actual presence of the community, and makes them able to support each other, letting the tree flourish again.

TREEHOUSE
Web App and Widget
1vNGC2020 19.07.2020

SILVIA BREMBATI
PRODUCT DESIGNER
silvia.brembati@mail.polimi.it
BENEDETTA BOLIS
ENGINEERING PHYSICS STUDENT
benedetta.bolis@mail.polimi.it
virtualNGC 2020

- Filippo Carloni, M.Sc. student in Computer Science and Engineering Expressions (REs) are widely used to find patterns among data, like in genomic markers research for DNA analysis, signature-based detection for network intrusion detection systems, or search engines. TiReX is a novel and efficient RE matching architecture for FPGAs, based on the concept of matching core. RE passes into the compilation and optimization phase to be efficiently translated into sequences of basic matching instructions that a matching core runs on input data, and can be replaced to change the RE to be matched.

Embedding based knowledge graph link prediction for drug repurposing

NECST Lab @ Politecnico di Milano

- Edoardo Ramalli, M.Sc. student in Computer Science and Engineering Drug Repurposing is the investigation of existing drugs on the pharmaceutical market for new therapeutic purposes; drug repurposing reduces the time and cost of clinical trial steps, saving years, and billions of dollars in R&D. Identifying new diseases on which a drug can be effective is a complex problem: our approach leverages knowledge graphs (KG), networks composed of many types of entities and relations, on which embedding and graph completion techniques can be applied to infer insights and analyses. Our KG is built from well-known databases such as DrugBank, UniProt, and CTD and contains over one million relationships between more than 70K biological and pharmaceutical entities like diseases, genes, proteins and drugs. In this work, we research the applicability of knowledge graph completion techniques, such as link prediction (and triple classification) using a various number of different embedding models from different families: matrix factorization, geometric and Deep learning. Using these models is possible to infer new drug-disease relationships on our KG, and identify novel drug repurposing candidates. Preliminary experimental results are encouraging and show how state-of-the-art machine learning models, combined with the ever-growing amount of biological data freely available to the research community, could significantly improve the field of drug repurposing.

PLASTER - PYNQ-based abandoned object detection using a map-reduce approach o...

NECST Lab @ Politecnico di Milano

- Daniele Valentino de Vincenti, B.Sc. graduate in Biomedical Engineering @Politecnico di Milano - Lorenzo Farinelli, B.Sc. graduate in Computer Science and Engineering @Politecnico di Milano Plaster is a multi-layered infrastructure (based on C++) aimed at supporting the development of multi-FPGA systems and the management of large data flows between the nodes. In particular, the goal of the project is to provide the end-user with a set of tools (by the means of a Python library and a C++ service) to easily assign bitstreams to nodes and route data between them, in the context of a PYNQ-based cluster suitable for distributed acceleration of computation-intensive tasks. Using this platform, an abandoned objects detection tool is implemented, designed as a Multi-FPGA distributed system exploiting an hardware accelerated version of the YOLO neural network for image detection.

EMPhASIS - An EMbedded Public Attention Stress Identification System

NECST Lab @ Politecnico di Milano

Luns - Automatic lungs segmentation through neural network

NECST Lab @ Politecnico di Milano

- Irene Canavesi, B.Sc. student in Biomedical Engineering - Sara Caramaschi, B.Sc. student in Biomedical Engineering Lung cancer is one of the most frequently diagnosed cancer forms, with a mortality of 84.2% in 2018. Our project focuses on shortening diagnosis time and improving accuracy in the overall detection of this disease. We implemented a convolutional neural network capable of automatically identifying lungs on a CT image. Segmentation is a necessary first step for the development of an algorithm capable of identifying and classifying the tumor mass since errors in the ROI identification can lead to errors in the tumor mass recognition. The network architecture follows the structure of a preexisting network, the U-Net that performs well on medical images. We reached a very good test accuracy of 99.63%: the strength of our work lies in the large number of CT images of both healthy and sick patients, used for the training and validation of the network.

BlastFunction: How to combine Serverless and FPGAs

NECST Lab @ Politecnico di Milano

- Samuele Barbieri, B.Sc. student in Computer Science and Engineering The last decade saw cloud computing more and more involved as the primary technology to develop, deploy and maintain complex infrastructures and services at scale. This happened because cloud computing allows to consume resources on-demand and to dynamically scale performance. Some compute-intensive workloads require computing power that current CPUs are not able to provide and, for this reason, heterogeneous computing with FPGAs is becoming an interesting solution to continue to meet SLAs. However, requests to cloud services can come at unpredictable rates and, for this reason, resources may be underutilized for significant portions of time. To increase resource utilization, we propose BlastFunction, which is a system that allows to accelerate compute-intensive kernels with shared FPGAs handled in a serverless fashion, while reaching near-native execution latency. In this talk we will present the main aspects of BlastFunction, showing its capabilities to time-share FPGAs across multiple function instances to optimize devices utilization. We will also show how we implemented the sharing and orchestration mechanism on a Kubernetes cluster based on the Amazon Web Services (AWS) EC2 F1 instances.

Maeve - Fast genome analysis leveraging exact string matching

NECST Lab @ Politecnico di Milano

- Sofia Breschi, B.Sc. student in Biomedical Engineering - Beatrice Branchini, B.Sc. student in Biomedical Engineering In the last few years, the use of Next Generation Sequencing technology in medicine has become more and more common, in particular for the diagnosis of genetic diseases and the production of personalized drugs. In this context, the identification of characteristic patterns in the human genome plays an important role. Exact pattern matching algorithms are an efficient way to identify those sequences. However, this process represents a bottleneck in the genomic field as it is very computationally intensive and time-consuming. Moreover, general-purpose architectures are not optimized to handle the huge amount of data and operations used in a genomics context. Due to these considerations, we propose an implementation of the Knuth-Morris-Pratt (KMP) algorithm on FPGA, a particular family of integrated circuits capable of reconfiguration for an infinite number of times. The KMP algorithm results in being very fast and efficient, by reducing unnecessary comparisons of characters that have already been matched. Furthermore, to achieve an overall speedup of the alignment process, the implementation on FPGA will bring on an even faster and more efficient solution, thus providing the patient with a quick response.

EMoCy - Emotions Monitoring via wearable Computing System

NECST Lab @ Politecnico di Milano

- Ana Bogdanovic, M. Sc. student in Biomedical Engineering - Lorenzo Gecchelin, M. Sc. student in Design & Engineering - Anisia Lauditi, M. Sc. student in Biomedical Engineering - Noemi Gozzi, M. Sc. student in Biomedical Engineering - Armando Bellante, M. Sc. student in Computer Science & Engineering - Letizia Bergamasco, M. Sc. student in ICT for Smart Societies - Moaad Khamlich, M. Sc. student in Computational Engineering Stress is a psycho-physical response to very different loads, of an emotional, cognitive or social nature, which is perceived as excessive thus having severe implications on wellbeing both in the short and in the long term. Different physiological manifestations occur during stressful events which, if detected promptly, can help in managing the situation. Therefore, the objective of this project is to develop a small portable device for psychological stress detection. This includes design of machine learning framework for stress detection and a prototype of a low-cost portable device for recording the physiological data. The ML framework is including the model together with the heuristic and knowledge based feature engineering from physiological time series. As a result EMoCy system is achieving accuracy of 97.2 ± 2% on stress/baseline binary classification task.

Approximate Personalized PageRank on FPGA .

NECST Lab @ Politecnico di Milano

- Francesco Sgherzi, Computer Science [and Engineering] @Politecnico di Milano and University of Illinois at Chicago - Alberto Parravicini, PhD studenti in Computer Science @Politecnico di Milano Personalized Pagerank (PPR) is a common building block of Recommender Systems. In this setting, the computation of the topmost ranked vertices needs to be executed extremely fast, with low latency and possibly for multiple elements concurrently. In this work, we present a high throughput implementation of the PPR algorithm leveraging a reduced precision-fixed point computation in order to achieve up to 6x speedup and 42x lower energy consumption with respect to a state of the art CPU implementation.

NECSTTechTalk: La didattica del Politecnico di Milano (e non solo!) ai tempi ...

NECST Lab @ Politecnico di Milano

Il secondo semestre dell’A.A. 2019/20 non lo scorderemo tanto facilmente: nel momento di pausa tra la fine degli appelli d’esame e la ripartenza delle lezioni il mondo ci è cambiato sotto gli occhi. All’improvviso abbiamo dovuto reinventarci un modo per erogare la didattica del secondo semestre, per consentire ai nostri ragazzi di laurearsi, per far loro sostenere gli esami. In poco meno di due settimane il nostro Ateneo è riuscito a convertire da didattica in presenza a online circa 1400 insegnamenti: una massiccia prova di resilienza e adattamento al cambiamento. Non è stato facile, ha presentato molti problemi, ma è anche una straordinaria opportunità che adesso dobbiamo imparare a cogliere, per non perdere quanto di buono (ed è parecchio) è stato fatto in questi mesi complicati. Mesi in cui, per citare lo storico Yuval Harari decisioni che in tempi normali richiederebbero anni di attenta valutazione sono state approvate nel giro di poche ore.

ReWArDS - NECSTTechTalk 11/06/2020

NECST Lab @ Politecnico di Milano

Hardware accelerators are an effective solution to increase the performance of algorithms in a wide array of disciplines, from Data Science to Scientific Calculus. However, data scientists and mathematicians often do not have the required knowledge or time to fully exploit these accelerators, and they perceive them as difficult and frustrating to use. Furthermore, Artificial Neural Networks are becoming the base of many of these applications, both in embedded and in server-class contexts. While Graphics Processing Units (GPUs) are predominantly used for training, solutions for inference often rely on Field Programmable Gate Arrays (FPGAs) since they are more flexible and cost-efficient in many scenarios. The main goal is employing FPGA systems for processing huge quantities of data, that may be analyzed in real time, in order to provide a ready-to-use valuable solution to data scientists and mathematicians and applying FPGA systems to machine learning and AI applications to have a power efficient solution for complicated analytics problems.

A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020

NECST Lab @ Politecnico di Milano

Abstract inglese: In recent years Graphic Processing Units have seen widespread adoption in many scientific fields, from Machine Learning (ML) to Genomics. Their use makes it possible to achieve significant speedups and improvements in power efficiency over computationally intensive algorithms compared to General Purpose Central Processing Units. However, algorithms require specific knowledge of the GPU architecture and expertise to achieve significant results. In this work, we describe a methodology for automatic GPU kernel optimization. Our methodology exploits the Berkeley Roofline Model to perform a performance analysis of the algorithm considered and aims to increase the accessibility of GPU programming automatizing the optimization process of the kernel. We provide an in-depth analysis of this methodology, an overview on the state of the art, and a description of a tool we developed that automatically applies our methodology to obtain a highly optimized GPU version of two of the most popular algorithms used in computational biology, the X-drop and Smith-Waterman algorithms. The Smith-Waterman algorithm is one of the most used algorithms in genomics pipelines. The algorithm finds the optimal local alignment between two genomic sequences, at the cost of being particularly compute-intensive. The popular X-drop algorithm reduces the time required by the alignment by searching only for high-quality alignments. The algorithms accelerated using our methodology achieve more than 6x and 3x speed-up, for the X-drop and Smith-Waterman algorithms respectively, with respect to the state of the art implementation of these algorithms.

DRACO - NECSTTechTalk 28/05/2020

NECST Lab @ Politecnico di Milano

In recent years, there has been a shift in the computer architecture landscape. Moore’s Law and Dennard’s Scaling has driven for 40 years the research on General Purpose RISC-based architectures and their optimizations, thanks to scaling in transistor count and power budget. However, with the end of these laws, the researchers are close to the boundary for technological and architectural reasons. Hence, computer architects have to consider a new way to think computer architectures narrowing the domain of the targeted computations, focusing on Domain Specific Architectures (DSAs) to harvest more energy-efficient calculations.DSAs are architectures not particularly suited for general computations, but engines that perform few tasks efficiently. Nonetheless, for a long time, developing a full custom ASIC has been an effort and time-consuming process where the time to market and the Non-Recurrent engineering costs (NRE) are critical and not negligible. Indeed, in this scenario, productivity becomes a crucial point. This is why both companies and the academic world are trying to apply iterative approaches (e.g., Agile) to fast hardware development. Therefore, fast prototyping architectures and adaptable computing platform are becoming more and more important. Indeed, Field Programmable Gate Arrays (FPGAs) are the state of the art platform for reconfigurable computing. They can provide a generic system, and recently also heterogeneous, able to be reprogrammed at the circuit level after manufacturing. In fact, FPGAs are mainly used for fast prototyping of digital systems and architectures that need to be reprogrammed easily, e.g., telecommunication field and many others.

HYPPO - NECSTTechTalk 23/04/2020

NECST Lab @ Politecnico di Milano

Energy proportionality is the key in order to reduce the Total Cost of Ownership (TCO) of Warehouse Scale Computer (WSC) systems, yet is difficult to achieve in practice. Typical WSC hardware usually does not meet this principle. Furthermore, critical services (e.g. billing) require all servers to remain up regardless the current traffic intensity. These two issues make existing power management technique ineffective at reducing energy use in a WSC dimension. We present Hybrid Performance-aware Power-capping Orchestrator (HyPPO), a distributed Observe Decide Act (ODA) control loop for optimizing energy proportionality of a distribute containerized infrastructures. This first version of HyPPO uses Kubernetes resource metrics (e.g. milli-cpus consumption) in order to dynamically adjust node power consumption, while respecting the Service Level Agreement (SLA) agreement defined by the containerized application owners.

ECCO: An Electron Counting Implementation for Image Compression and Optimizat...

NECST Lab @ Politecnico di Milano

Outstanding advancements in imaging technology have made cryogenic electron microscopy a powerful technique for the nanocharacterization of biological macromolecular complexes, reaching atomic levels of resolution and being applicable to a wider set of samples than the other competing technologies. The real breakthrough in the development of cryo-EM has happened less than a decade ago, with the introduction of direct detection devices. These cameras allow unprecedented speed and resolution, and Lawrence Berkeley National Lab is developing a new detector, the 4D camera, that can operate at 87000 frames per second, revealing exclusive temporal dynamics of the investigated processes. The current bottlenecks of the 4D camera, however, are the management of the large amount of data generated (around 50 GB/s) and the intrinsic noise level characterizing the signal acquired at that speed. Yet, the high frame rate enables the recognition of single electrons when they strike the detector, as opposed to traditional electron microscopy, where the charge is cumulated for every frame. Electron counting has remarkable advantages since it completely rejects electrical background noise as well as the variability in the electron charge deposition phenomena and it dramatically compresses images by saving them as lists of events coordinates. With this work, the counting efficiency of the algorithm is enhanced, through the introduction of a denoising step before thresholding out the background noise, rising the precision by 7.11% with respect to the reference implementation. Furthermore, the localization of the events is refined to allow super-resolution, and a classification step is added to reduce the is- sue of collision losses, caused by overlapping electrons. In the end, a 10000x compression ratio is achieved thanks to electron counting. A GPU acceleration of the final algorithm is also proposed, achieving, in the best case, a speed up of 284x. The timing performances of the developed tool, in fact, are crucial for its real time execution on the microscope output. Ultimately, this work aims at enabling a more efficient data management between the microscopy center and the supercomputing facility, both involved in the data processing pipeline, by moving part of the computation towards the instrumentation and transferring only a compressed version of the datasets. The intelligent redistribution of workloads, in fact, removes the bottleneck in data transfer and grants the use of the microscope at its maximum frame rate.

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