Sökning: "Open source mjukvara"

Visar resultat 1 - 5 av 63 uppsatser innehållade orden Open source mjukvara.

  1. 1. Low-power Implementation of Neural Network Extension for RISC-V CPU

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Dario Lo Presti Costantino; [2023]
    Nyckelord :Artificial intelligence; Deep learning; Neural networks; Edge computing; Convolutional neural networks; Low-power electronics; RISC-V; AI accelerators; Parallel processing; Artificiell intelligens; Deep learning; Neurala nätverk; Edge computing; konvolutionella neurala nätverk; Lågeffektelektronik; RISC-V; AI-acceleratorer; Parallell bearbetning;

    Sammanfattning : Deep Learning and Neural Networks have been studied and developed for many years as of today, but there is still a great need of research on this field, because the industry needs are rapidly changing. The new challenge in this field is called edge inference and it is the deployment of Deep Learning on small, simple and cheap devices, such as low-power microcontrollers. LÄS MER

  3. 2. An Open-Source Autoencoder Compression Tool for High Energy Physics

    Magister-uppsats, Lunds universitet/Partikel- och kärnfysik; Lunds universitet/Fysiska institutionen

    Författare :Axel Gallén; [2023]
    Nyckelord :Physics; Particle Physics; Analysis; Machine Learning; Neural Networks; Autoencoders; Data Compression; Lossy Compression; Baler; Physics and Astronomy;

    Sammanfattning : A common problem across scientific fields and industries is data storage. This thesis presents an open-source lossy data compression tool with its foundation in Machine Learning - Baler. Baler has been used to compress High Energy Physics (HEP) data, and initial compression tests on Computational Fluid Dynamics (CFD) toy data have been performed. LÄS MER

  4. 3. Analysis of Flow Prolongation Using Graph Neural Network in FIFO Multiplexing System

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Weiran Wang; [2023]
    Nyckelord :Network Calculus; Flow Prolongation; Graph Neural Network; Fast Gradient Sign Method; Delay Bound; Nätverkskalkyl; Flödesförlängning; Graph Neural Network; Fast Gradient Sign Method; Fördröjningsgräns;

    Sammanfattning : Network Calculus views a network system as a queuing framework and provides a series of mathematical functions for finding an upper bound of an end-to-end delay. It is crucial for the design of networks and applications with a hard delay guarantee, such as the emerging Time Sensitive Network. LÄS MER

  5. 4. The temporal side of pull request acceptance

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Balthazar West; [2023]
    Nyckelord :Pull requests; GitHub; code review; machine learning; survey; Pull requests; GitHub; kodrecension; maskininlärning; enkät;

    Sammanfattning : The contemporary way of contributing to open-source software is through online platforms. GitHub is the most widely used platform for this purpose. On GitHub, users can suggest improvements to projects by opening a pull request (PR), taking on the role of the submitter. LÄS MER

  7. 5. Adopting Observability-Driven Development for Cloud-Native Applications : Designing End-to-end Observability Pipeline using Open-source Software

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Chujie Ni; [2023]
    Nyckelord :Observability-driven Development; End-to-end observability; Open-source pipeline; OpenTelemetry; Kubernetes; Observerbarhetsdriven utveckling; observerbarhet från slut till slut; pipeline med öppen källkod; OpenTelemetry; Kubernetes;

    Sammanfattning : As cloud-native applications become more distributed, complex, and unpredictable with the adoption of microservices and other new architectural components, traditional monitoring solutions are inadequate in providing end-to-end visibility and proactively identifying deviations from expected behaviour before they become disruptive to services. In response to these challenges, observability-driven development (ODD) is proposed as a new methodology that leverages tools and practices to observe the state and detect the behaviour of systems. LÄS MER