Ponències/Comunicacions de congressos
Enviaments recents
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δLTA:: Decoupling camera sampling from processing to avoid redundant computations in the vision pipeline
(Association for Computing Machinery (ACM), 2023)
Text en actes de congrés
Accés obertContinuous Vision (CV) systems are essential for emerging applications like Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR). A standard CV System-on-a-Chip (SoC) pipeline includes a frontend for image capture ... -
SLIDEX: Sliding window extension for image processing
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Text en actes de congrés
Accés obertWith the rising need for efficient image processing in emerging applications such as Autonomous Driving (AD) and Augmented/Virtual Reality (AR/VR), many existing solutions do not meet their performance and energy efficiency ... -
QeiHaN: An energy-efficient DNN accelerator that leverages log quantization in NDP architectures
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Comunicació de congrés
Accés obertThe constant growth of DNNs makes them challenging to implement and run efficiently on traditional computecentric architectures. Some works have attempted to enhance accelerators by adding more compute units and on-chip ... -
Boustrophedonic frames: Quasi-optimal L2 caching for textures in GPUs
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Text en actes de congrés
Accés obertLiterature is plentiful in works exploiting cache locality for GPUs. A majority of them explore replacement or bypassing policies. In this paper, however, we surpass this exploration by fabricating a formal proof for a ... -
Exploiting kernel compression on BNNs
(Institute of Electrical and Electronics Engineers (IEEE), 2023)
Text en actes de congrés
Accés obertBinary Neural Networks (BNNs) are showing tremen-dous success on realistic image classification tasks. Notably, their accuracy is similar to the state-of-the-art accuracy obtained by full-precision models tailored to edge ... -
K-D Bonsai: ISA-extensions to compress K-D trees for autonomous driving tasks
(Association for Computing Machinery (ACM), 2023)
Text en actes de congrés
Accés obertAutonomous Driving (AD) systems extensively manipulate 3D point clouds for object detection and vehicle localization. Thereby, efficient processing of 3D point clouds is crucial in these systems. In this work we propose ... -
Lightweight register file caching in collector units for GPUs
(Association for Computing Machinery (ACM), 2023)
Text en actes de congrés
Accés obertModern GPUs benefit from a sizable Register File (RF) to provide fine-grained thread switching. As the RF is huge and accessed frequently, it consumes a considerable share of the dynamic energy of the GPU. Designing a ... -
Simple out of order core for GPGPUs
(Association for Computing Machinery (ACM), 2023)
Text en actes de congrés
Accés obertGPU architectures have become popular for executing general-purpose programs which rely on having a large number of threads that run concurrently to hide the latency among dependent instructions. This approach has an ... -
Sliding window support for image processing in autonomous vehicles
(2022)
Text en actes de congrés
Accés obertCamera-based autonomous driving extensively ma-nipulates images for object detection, object tracking, or camera-based localization tasks. Therefore, efficient and fast image processing is crucial in those systems. ... -
DTexL: Decoupled raster pipeline for texture locality
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Text en actes de congrés
Accés obertContemporary GPU architectures have multiple shader cores and a scheduler that distributes work (threads) among them, focusing on load balancing. These load balancing techniques favor thread distributions that are detrimental ... -
A programmable accelerator for streaming automatic speech recognition on edge devices
(2022)
Text en actes de congrés
Accés obertAutomatic Speech Recognition (ASR) is quickly becoming a mainstream technology, mainly driven by the outstanding accuracy achieved by modern systems based on machine learning. However, these systems often require billions ... -
XFeatur: Hardware feature extraction for DNN auto-tuning
(Institute of Electrical and Electronics Engineers (IEEE), 2022)
Text en actes de congrés
Accés obertIn 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 ...