FPGA-accelerated machine learning inference as a service for particle physics computing

New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table

The EPICS Software Framework Moves from Controls to Physics

The Experimental Physics and Industrial Control System (EPICS), is an open-source software framework for high-performance distributed control, and is at the heart of many of the world’s large accelerators and telescopes. Recently, EPICS has undergone a major revision, with the aim of better computing supporting for the next generation of machines and analytical tools. Many new data types, such as matrices, tables, images, and statistical descriptions, plus users’ own data types, now supplement the simple scalar and waveform types of the former EPICS. New computational architectures for scientific computing have been added for high-performance data processing services and pipelining. Python and Java bindings have enabled powerful new user interfaces. The result has been that controls are now being integrated with modelling and simulation, machine learning, enterprise databases, and experiment DAQs. We introduce this new EPICS (version 7) from the perspective of accelerator physics and review early adoption cases in accelerators around the world

Architecture, design and source code comparison of ns-2 and ns-3 network simulators

Ns-2 and its successor ns-3 are discrete-event simulators. Ns- 3 is still under development, but offers some interesting characteristics for developers while ns-2 still has a big user base. This paper remarks current differences between both tools from developers point of view. Leaving performance and resources consumption aside, technical issues described in the present paper might help to choose one or another alternative depending of simulation and project management requirements.Ministerio de Educación y Ciencia TIN2006-15617-C03-03Junta de Andalucía P06-TIC-229

ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers

Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services. Despite the advancement of ASR, however, most publicly available call-based speech corpora such as Switchboard are old-fashioned. Also, most existing call corpora are in English and mainly focus on open domain dialog or general scenarios such as audiobooks. Here we introduce a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people, i.e., ClovaCall corpus. ClovaCall includes approximately 60,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain. We validate the effectiveness of our dataset with intensive experiments using two standard ASR models. Furthermore, we release our ClovaCall dataset and baseline source codes to be available via https://github.com/ClovaAI/ClovaCall.Comment: 5 pages, 2 figures, 4 tables, The first two authors equally contributed to this wor

Uncertainty Analysis for Data-Driven Chance-Constrained Optimization

In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.BMWi, 0350013A, ChemEFlex - Umsetzbarkeitsanalyse zur Lastflexibilisierung elektrochemischer Verfahren in der Industrie; Teilvorhaben: Modellierung der Chlor-Alkali-Elektrolyse sowie anderer Prozesse und deren Bewertung hinsichtlich Wirtschaftlichkeit und möglicher HemmnisseDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

SYGMA: Stellar Yields for Galactic Modeling Applications

The stellar yields for galactic modeling applications (SYGMA) code is an open-source module that models the chemical ejecta and feedback of simple stellar populations (SSPs). It is intended for use in hydrodynamical simulations and semi-analytic models of galactic chemical evolution. The module includes the enrichment from asymptotic giant branch (AGB) stars, massive stars, SNIa and neutron-star mergers. An extensive and extendable stellar yields library includes the NuGrid yields with all elements and many isotopes up to Bi. Stellar feedback from mechanic and frequency-dependent radiative luminosities are computed based on NuGrid stellar models and their synthetic spectra. The module further allows for customizable initial-mass functions and supernova Ia (SNIa) delay-time distributions to calculate time-dependent ejecta based on stellar yield input. A variety of r-process sites can be included. A comparison of SSP ejecta based on NuGrid yields with those from Portinari et al. (1998) and Marigo (2001) reveals up to a factor of 3.5 and 4.8 less C and N enrichment from AGB stars at low metallicity, a result we attribute to NuGrid's modeling of hot-bottom burning. Different core-collapse supernova explosion and fallback prescriptions may lead to substantial variations for the accumulated ejecta of C, O and Si in the first $10^7\, \mathrm{yr}$ at $Z=0.001$. An online interface of the open-source SYGMA module enables interactive simulations, analysis and data extraction of the evolution of all species formed by the evolution of simple stellar populations.Comment: 18 pages, 10 figures, 3 tables, published in ApJ