1,721,106 research outputs found
Machine Learning inference using PYNQ environment in a AWS EC2 F1 Instance
Full text linkIn the past few years, using Machine and Deep Learning techniques has become more and more viable, thanks to the availability of tools which make the need of specific knowledge in the realm of data science and complex networks less vital to achieve a satisfactory final result in a variety of research fields. This process has caused an explosion in the adoption of such techniques, e.g. in the context of High Energy Physics.
The range of applications for ML becomes even larger if we consider the implementation of these algorithms on low-latency hardware like FPGAs which promise smaller latency with respect to traditional inference algorithms running on general purpose CPUs.
This paper presents and discusses the activity running at the University of Bologna and INFN-Bologna where a new open-source project from Xilinx called PYNQ is being tested. Its purpose is to grant designers the possibility to exploit the benefits of programmable logic and microprocessors using the Python language and libraries. This new software environment can be deployed on a variety of Xilinx platforms, from the simplest ones like ZYNQ boards, to more advanced and high performance ones, like Alveo accelerator cards and AWS EC2 F1 instances.
The use of cloud computing in this work lets us test the capabilities of this new workflow, from the creation and training of a Neural Network and the creation of a HLS project using HLS4ML, to testing the predictions of the NN using PYNQ APIs and functions written in Pytho
Deep Learning fast inference on FPGA for CMS Muon Level-1 Trigger studies
Full text linkWith the advent of the High-Luminosity phase of the LHC (HL-LHC), the instantaneous luminosity of the Large Hadron Collider at CERN is expected to increase up to ≈7.5⋅1034cm−2s−1. Therefore, new strategies for data acquisition and processing will be necessary, in preparation for the higher number of signals produced inside the detectors. In the context of an upgrade of the trigger system of the Compact Muon Solenoid (CMS), new reconstruction algorithms, aiming for an improved performance, are being developed. For what concerns the online tracking of muons, one of the figures that is being improved is the accuracy of the transverse momentum (pT) measurement. Machine Learning techniques have already been considered as a promising solution for this problem, as they make possible, with the use of more information collected by the detector, to build models able to predict with an improved precision the pT. This work aims to implement such models onto an FPGA, which promises smaller latency with respect to traditional inference algorithms running on CPU, an important aspect for a trigger system. The analysis carried out in this work will use data obtained through Monte Carlo simulations of muons crossing the barrel region of the CMS muon chambers, and compare the results with the pT assigned by the current CMS Level 1 Barrel Muon Track Finder (BMTF) trigger system
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A 1 GS/s sampling digitizer designed with interleaved architecture (GSPS) for the LaBr3 detectors of the FAMU experiment
No full textA fast continuous sampling digitizer has been designed to acquire fast scintillating detector signalfrom cerium activated lantanum bromide (LaBr3(Ce)) scintillation crystal. These are foreseenin the FAMU experiment which is aimed at spectroscopic measurements of muonic hydrogen,possibly providing insights into the so-called proton radius puzzle. The board, named GSPS,is implemented as an FMC mezzanine which hosts two off-the-shelf sampling ADC used in in-terleaved timing architecture, achieving a 1 GS/s sampling rate (with a 12-bit nominal accuracyover the first Nyquist interval of frequencies, ranging from DC to 500 MHz). Interleaved tech-nique allowed us to keep both lower production costs and simple acquisition system avoidingcomplex interface protocols like JESD204. The board will be described; the test setup and theused methodology to characterize the device will be explained; the achieved performances willbe shown and discussed. In particular it will be pointed out how the two interleaved ADCs can becalibrated in order to get the best performance. The different contributions to both noise and dis-tortion affecting the device will be analyzed applying general techniques for high-sampling-speedcontinuous ADCs. Lastly, future improvements will be introduced: in particular, the solutions weforesee to get a effective number of bits of about 10 over the whole first Nyquist interval will beenlightened
Accelerating Machine Learning inference using FPGAs: the PYNQ framework tested on an AWS EC2 F1 Instance
Full text linkIn the past few years, using Machine and Deep Learning techniques has become more and more viable, thanks to the availability of tools which allow people without specific knowledge in the realm of data science and complex networks to build AIs for a variety of research fields. This process has encouraged the adoption of such techniques, e.g. in the context of High Energy Physics.
In order to facilitate the translation of Machine Learning (ML) models to fit in the usual workflow for programming FPGAs, a variety of tools have been developed. One example is the HLS4ML toolkit, which allows the translation of Neural Networks (NN) built using tools like TensorFlow to a High-Level Synthesis description (e.g. C++) in order to implement this kind of ML algorithms on FPGAs.
This paper presents the activity running at the University of Bologna and INFN-Bologna devoted to preliminary studies for the trigger systems of the Compact Muon Solenoid experiment at the CERN LHC accelerator. An open-source project from Xilinx called PYNQ is being tested combined with the HLS4ML toolkit. The PYNQ purpose is to grant designers the possibility to exploit the benefits of programmable logic and microprocessors using the Python language.
The use of cloud computing in this work allows us to test the capabilities of this workflow, from the creation and training of a Neural Network and the creation of a HLS project using HLS4ML, to managing NN inference with custom Python drivers.
The main application explored in this work lives in the context of the trigger system of the CMS, where new reconstruction algorithms are being developed due to the advent of the High-Luminosity phase of the LHC
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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