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GeForce
Publications associées (19)
Compilation and Design Space Exploration of Dataflow Programs for Heterogeneous CPU-GPU Platforms
Aurélien François Gilbert Bloch
Today's continued increase in demand for processing power, despite the slowdown of Moore's law, has led to an increase in processor count, which has resulted in energy consumption and distribution problems. To address this, there is a growing trend toward ...
EPFL2023Inter-actions parallel execution on GPU from high-level dataflow synthesis
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Recent GPU architectures make available numbers of parallel processing units that exceed by orders of magnitude the ones offered by CPU architectures. Whereas programs written using dataflow programming languages are well suited for programming heterogeneo ...
IEEE2022Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation
Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...
EPFL2022Equinox: Training (for Free) on a Custom Inference Accelerator
Babak Falsafi, Martin Jaggi, Louis Coulon, Ahmet Caner Yüzügüler, Mario Paulo Drumond Lages De Oliveira, Arash Pourhabibi Zarandi
DNN inference accelerators executing online services exhibit low average loads because of service demand variability, leading to poor resource utilization. Unfortunately, reclaiming idle inference cycles is difficult as other workloads can not execute on a ...
ACM2021A review of CUDA optimization techniques and tools for structured grid computing
Recent advances in GPUs opened a new opportunity in harnessing their computing power for general purpose computing. CUDA, an extension to C programming, is developed for programming NVIDIA GPUs. However, efficiently programming GPUs using CUDA is very tedi ...
2020High Performance Computing for gravitational lens modeling: Single vs double precision on GPUs and CPUs
Jean-Paul Richard Kneib, Markus Rexroth
Strong gravitational lensing is a powerful probe of cosmology and the dark matter distribution. Efficient lensing software is already a necessity to fully use its potential and the performance demands will only increase with the upcoming generation of tele ...
ELSEVIER2020A Hybrid Cache HW/SW Stack for Optimizing Neural Network Runtime, Power and Endurance
David Atienza Alonso, Marina Zapater Sancho, Alexandre Sébastien Julien Levisse, William Andrew Simon
Hybrid caches consisting of both SRAM and emerging Non-Volatile Random Access Memory (eNVRAM) bitcells increase cache capacity and reduce power consumption by taking advantage of eNVRAM's small area footprint and low leakage energy. However, they also inhe ...
2020GPU-Accelerated Finite Volume Particle Simulation of Free Jet Deviation by Multi-jet Rotating Pelton Runner
Numerical simulation of Pelton turbine hydrodynamics is helpful to identify the energy loss mechanisms in the runner and minimize their effect. However, it is a challenging task that involves handling the unsteady free surface flow and moving boundaries re ...
EPFL2019Hardware-conscious Query Processing in GPU-accelerated Analytical Engines
Anastasia Ailamaki, Periklis Chrysogelos, Panagiotis Sioulas
In order to improve their power efficiency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the nece ...
2019An Accelerator for High Efficient Vision Processing
Paolo Ienne, Robert Fasthuber, Zidong Du, Qi Guo
In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency a ...
Ieee-Inst Electrical Electronics Engineers Inc2017