Ponències/Comunicacions de congressos: Enviaments recents

Ara es mostren els items 13-24 de 187

    • XFeatur: Hardware feature extraction for DNN auto-tuning 

      Sierra Acosta, Jorge; Diavastos, Andreas; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2022)
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      In this work, we extend the auto-tuning process of the state-of-the-art TVM framework with XFeatur; a tool that extracts new meaningful hardware-related features that improve the quality of the representation of the search ...

    • MEGsim: A Novel methodology for efficient simulation of graphics workloads in GPUs 

      Ortiz Escribano, Jorge; Corbalán Navarro, David; Aragón Alcaraz, Juan Luis; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2022)
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      An important drawback of cycle-accurate microarchitectural simulators is that they are several orders of magnitude slower than the system they model. This becomes an important issue when simulations have to be repeated ...

    • DTM-NUCA: dynamic texture mapping-NUCA for energy-efficient graphics rendering 

      Corbalán Navarro, David; Aragón, Juan Luis; Parcerisa Bundó, Joan Manuel; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2022)
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      Modern mobile GPUs integrate an increasing number of shader cores to speedup the execution of graphics workloads. Each core integrates a private Texture Cache to apply texturing effects on objects, which is backed-up by a ...

    • TCOR: a tile cache with optimal replacement 

      Joseph, Diya; Aragón, Juan Luis; Parcerisa Bundó, Joan Manuel; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2022)
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      Cache Replacement Policies are known to have an important impact on hit rates. The OPT replacement policy [27] has been formally proven as optimal for minimizing misses. Due to its need to look far ahead for future memory ...

    • Improving the energy efficiency of the graphics pipeline by reducing overshading 

      Corbalán Navarro, David; Aragón, Juan Luis; Anglada Sánchez, Martí; de Lucas Casamayor, Enrique; Parcerisa Bundó, Joan Manuel; González Colás, Antonio María (2021)
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      The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is determining which parts of every object are visible in the final image. There are different approaches to solve the ...

    • A low-power hardware accelerator for ORB feature extraction in self-driving cars 

      Taranco Serna, Raúl; Arnau Montañés, José María; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2021)
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      Simultaneous Localization And Mapping (SLAM) is a key component for autonomous navigation. SLAM consists of building and creating a map of an unknown environment while keeping track of the exploring agent's location within ...

    • Boosting LSTM performance through dynamic precision selection 

      Silfa Feliz, Franyell Antonio; Arnau Montañés, José María; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2020)
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      The use of low numerical precision is a fundamental optimization included in modern accelerators for Deep Neural Networks (DNNs). The number of bits of the numerical representation is set to the minimum precision that is ...

    • Demystifying power and performance bottlenecks in autonomous driving systems 

      Exenberger Becker, Pedro Henrique; Arnau Montañés, José María; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2020)
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      Autonomous Vehicles (AVs) have the potential to radically change the automotive industry. However, computing solutions for AVs have to meet severe performance and power constraints to guarantee a safe driving experience. ...

    • DRAM errors in the field: a statistical approach 

      Živanovič, Darko; Esmaili Dokht, Pouya; Moré, Sergi; Bartolomé, Javier; Carpenter, Paul Matthew; Radojković, Petar; Ayguadé Parra, Eduard (Association for Computing Machinery (ACM), 2019)
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      This paper summarizes our two-year study of corrected and uncor-rected errors on the MareNostrum 3 supercomputer, covering 2000 billion MB-hours of DRAM in the field. The study analyzes 4.5 million corrected and 71 uncorrected ...

    • Neuron-level fuzzy memoization in RNNs 

      Silfa Feliz, Franyell Antonio; Dot Artigas, Gem; Arnau Montañés, José María; González Colás, Antonio María (Association for Computing Machinery (ACM), 2019)
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      Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the ...

    • Leveraging run-time feedback for efficient ASR acceleration 

      Yazdani, Reza; Arnau Montañés, José María; González Colás, Antonio María (Institute of Electrical and Electronics Engineers (IEEE), 2019)
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      In this work, we propose Locality-AWare-Scheme (LAWS) for an Automatic Speech Recognition (ASR) accelerator in order to significantly reduce its energy consumption and memory requirements, by leveraging the locality among ...

    • SCU: a GPU stream compaction unit for graph processing 

      Segura Salvador, Albert; Arnau Montañés, José María; González Colás, Antonio María (Association for Computing Machinery (ACM), 2019)
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      Graph processing algorithms are key in many emerging applications in areas such as machine learning and data analytics. Although the processing of large scale graphs exhibits a high degree of parallelism, the memory access ...