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  1. Article ; Online: Editorial: Efficient AI in particle physics and astrophysics.

    Duarte, Javier / Liu, Mia / Ngadiuba, Jennifer / Cuoco, Elena / Thaler, Jesse

    Frontiers in artificial intelligence

    2022  Volume 5, Page(s) 999173

    Language English
    Publishing date 2022-09-30
    Publishing country Switzerland
    Document type Editorial
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2022.999173
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning

    Gandrakota, Abhijith / Zhang, Lily / Puli, Aahlad / Cranmer, Kyle / Ngadiuba, Jennifer / Ranganath, Rajesh / Tran, Nhan

    2024  

    Abstract: Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection for high-energy ... ...

    Abstract Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection for high-energy physics. First, rather than train a generative model on the single most dominant background process, we build detection algorithms using representation learning from multiple background types, thus taking advantage of more information to improve estimation of what is relevant for detection. Second, we generalize decorrelation to the multi-background setting, thus directly enforcing a more complete definition of robustness for anomaly detection. We demonstrate the benefit of the proposed robust multi-background anomaly detection algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning ; High Energy Physics - Phenomenology ; Physics - Data Analysis ; Statistics and Probability
    Subject code 006
    Publishing date 2024-01-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: LHC physics dataset for unsupervised New Physics detection at 40 MHz.

    Govorkova, Ekaterina / Puljak, Ema / Aarrestad, Thea / Pierini, Maurizio / Woźniak, Kinga Anna / Ngadiuba, Jennifer

    Scientific data

    2022  Volume 9, Issue 1, Page(s) 118

    Abstract: In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk ... ...

    Abstract In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.
    Language English
    Publishing date 2022-03-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 2775191-0
    ISSN 2052-4463 ; 2052-4463
    ISSN (online) 2052-4463
    ISSN 2052-4463
    DOI 10.1038/s41597-022-01187-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

    Liu, Ryan / Gandrakota, Abhijith / Ngadiuba, Jennifer / Spiropulu, Maria / Vlimant, Jean-Roch

    2023  

    Abstract: Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a ... ...

    Abstract Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based variational autoencoder (VAE) anomaly detection algorithm. We demonstrate a 2x signal efficiency gain compared with traditional subjettiness-based jet selection. Furthermore, with an eye to the future deployment to trigger systems, we propose the CLIP-VAE, which reduces the inference-time cost of anomaly detection by using the KL-divergence loss as the anomaly score, resulting in a 2x acceleration in latency and reducing the caching requirement.

    Comment: 7 pages, 4 figures, accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning
    Publishing date 2023-11-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation

    Liu, Ryan / Gandrakota, Abhijith / Ngadiuba, Jennifer / Spiropulu, Maria / Vlimant, Jean-Roch

    2023  

    Abstract: The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low ... ...

    Abstract The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed. For deep learning models, this implies that only models with low computational complexity that have weak inductive bias are feasible. To address this issue, we utilize knowledge distillation to leverage both the performance of large models and the reduced computational complexity of small ones. In this paper, we present an implementation of knowledge distillation, demonstrating an overall boost in the student models' performance for the task of classifying jets at the LHC. Furthermore, by using a teacher model with a strong inductive bias of Lorentz symmetry, we show that we can induce the same inductive bias in the student model which leads to better robustness against arbitrary Lorentz boost.

    Comment: 7 pages, 3 figures, accepted at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
    Keywords High Energy Physics - Experiment ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-11-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows.

    Jawahar, Pratik / Aarrestad, Thea / Chernyavskaya, Nadezda / Pierini, Maurizio / Wozniak, Kinga A / Ngadiuba, Jennifer / Duarte, Javier / Tsan, Steven

    Frontiers in big data

    2022  Volume 5, Page(s) 803685

    Abstract: We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how ...

    Abstract We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.
    Language English
    Publishing date 2022-02-28
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2022.803685
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

    Ghielmetti, Nicolò / Loncar, Vladimir / Pierini, Maurizio / Roed, Marcel / Summers, Sioni / Aarrestad, Thea / Petersson, Christoffer / Linander, Hampus / Ngadiuba, Jennifer / Lin, Kelvin / Harris, Philip

    2022  

    Abstract: In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network ... ...

    Abstract In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.

    Comment: 11 pages, 6 tables, 5 figures
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Hardware Architecture ; Computer Science - Machine Learning ; Physics - Instrumentation and Detectors ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2022-05-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Ultra Low-latency, Low-area Inference Accelerators using Heterogeneous Deep Quantization with QKeras and hls4ml

    Coelho Jr., Claudionor N. / Kuusela, Aki / Zhuang, Hao / Aarrestad, Thea / Loncar, Vladimir / Ngadiuba, Jennifer / Pierini, Maurizio / Summers, Sioni

    2020  

    Abstract: In this paper, we introduce the QKeras library, an extension of the Keras library allowing for the creation of heterogeneously quantized versions of deep neural network models, through drop-in replacement of Keras layers. These models are trained ... ...

    Abstract In this paper, we introduce the QKeras library, an extension of the Keras library allowing for the creation of heterogeneously quantized versions of deep neural network models, through drop-in replacement of Keras layers. These models are trained quantization-aware, where the user can trade off model area or energy consumption by accuracy. We demonstrate how the reduction of numerical precision, through quantization-aware training, significantly reduces resource consumption while retaining high accuracy when implemented on FPGA hardware. Together with the hls4ml library, this allows for a fully automated deployment of quantized Keras models on chip, crucial for ultra low-latency inference. As a benchmark problem, we consider a classification task for the triggering of events in proton-proton collisions at the CERN Large Hadron Collider, where a latency of ${\mathcal O}(1)~\mu$s is required.

    Comment: 9 pages, 9 figures, 3 tables, submitted to ICCAD
    Keywords Physics - Instrumentation and Detectors ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing ; Electrical Engineering and Systems Science - Signal Processing ; High Energy Physics - Experiment
    Subject code 006
    Publishing date 2020-06-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Accelerating Recurrent Neural Networks for Gravitational Wave Experiments

    Que, Zhiqiang / Wang, Erwei / Marikar, Umar / Moreno, Eric / Ngadiuba, Jennifer / Javed, Hamza / Borzyszkowski, Bartłomiej / Aarrestad, Thea / Loncar, Vladimir / Summers, Sioni / Pierini, Maurizio / Cheung, Peter Y / Luk, Wayne

    2021  

    Abstract: This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as ... ...

    Abstract This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational interferometers such as the LIGO detectors capture cosmic events such as black hole mergers which happen at unknown times and of varying durations, producing time-series data. We have developed a new architecture capable of accelerating RNN inference for analyzing time-series data from LIGO detectors. This architecture is based on optimizing the initiation intervals (II) in a multi-layer LSTM (Long Short-Term Memory) network, by identifying appropriate reuse factors for each layer. A customizable template for this architecture has been designed, which enables the generation of low-latency FPGA designs with efficient resource utilization using high-level synthesis tools. The proposed approach has been evaluated based on two LSTM models, targeting a ZYNQ 7045 FPGA and a U250 FPGA. Experimental results show that with balanced II, the number of DSPs can be reduced up to 42% while achieving the same IIs. When compared to other FPGA-based LSTM designs, our design can achieve about 4.92 to 12.4 times lower latency.

    Comment: Accepted at the 2021 32nd IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP)
    Keywords Computer Science - Machine Learning ; Computer Science - Hardware Architecture ; Physics - Instrumentation and Detectors
    Subject code 006
    Publishing date 2021-06-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.

    Iiyama, Yutaro / Cerminara, Gianluca / Gupta, Abhijay / Kieseler, Jan / Loncar, Vladimir / Pierini, Maurizio / Qasim, Shah Rukh / Rieger, Marcel / Summers, Sioni / Van Onsem, Gerrit / Wozniak, Kinga Anna / Ngadiuba, Jennifer / Di Guglielmo, Giuseppe / Duarte, Javier / Harris, Philip / Rankin, Dylan / Jindariani, Sergo / Liu, Mia / Pedro, Kevin /
    Tran, Nhan / Kreinar, Edward / Wu, Zhenbin

    Frontiers in big data

    2021  Volume 3, Page(s) 598927

    Abstract: Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA- ... ...

    Abstract Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.
    Language English
    Publishing date 2021-01-12
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2020.598927
    Database MEDical Literature Analysis and Retrieval System OnLINE

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