Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Vissers, Kees"'
Autor:
Orosa, Lois, Koppula, Skanda, Umuroglu, Yaman, Kanellopoulos, Konstantinos, Gomez-Luna, Juan, Blott, Michaela, Vissers, Kees, Mutlu, Onur
Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applications such as image segmentation and high-resolution image generation.
Externí odkaz:
http://arxiv.org/abs/2202.02310
Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications, spanning from image classification to speech recognition. While providing excellent accuracy, they often have enormous compute and memory requirements. As a result
Externí odkaz:
http://arxiv.org/abs/2011.05873
Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that includes novel support for 2D SIMD datapaths and shuffle interconnection network. The current approach to programming the AI Engine relies on a C/C++ API for v
Externí odkaz:
http://arxiv.org/abs/2006.01331
Autor:
Gambardella, Giulio, Kappauf, Johannes, Blott, Michaela, Doehring, Christoph, Kumm, Martin, Zipf, Peter, Vissers, Kees
Publikováno v:
2019 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT)
Neural Networks are currently one of the most widely deployed machine learning algorithms. In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evaluated for deployment in safety critical applications such as self drivi
Externí odkaz:
http://arxiv.org/abs/1912.07394
Akademický článek
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Autor:
Qasaimeh, Murad, Denolf, Kristof, Lo, Jack, Vissers, Kees, Zambreno, Joseph, Jones, Phillip H.
Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embedded computer vision (e.g. multi-core CPUs, GPUs, and FPGAs), and thei
Externí odkaz:
http://arxiv.org/abs/1906.11879
Autor:
Yang, Yifan, Huang, Qijing, Wu, Bichen, Zhang, Tianjun, Ma, Liang, Gambardella, Giulio, Blott, Michaela, Lavagno, Luciano, Vissers, Kees, Wawrzynek, John, Keutzer, Kurt
Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a result, the key application characteristics such as frames-per-second (FP
Externí odkaz:
http://arxiv.org/abs/1811.08634
Autor:
Fraser, Nicholas J., Umuroglu, Yaman, Gambardella, Giulio, Blott, Michaela, Leong, Philip, Jahre, Magnus, Vissers, Kees
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, which contain an abundance of fine
Externí odkaz:
http://arxiv.org/abs/1701.03400
Autor:
Umuroglu, Yaman, Fraser, Nicholas J., Gambardella, Giulio, Blott, Michaela, Leong, Philip, Jahre, Magnus, Vissers, Kees
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values. In this paper, we present FINN,
Externí odkaz:
http://arxiv.org/abs/1612.07119
Autor:
Qasaimeh, Murad, Denolf, Kristof, Khodamoradi, Alireza, Blott, Michaela, Lo, Jack, Halder, Lisa, Vissers, Kees, Zambreno, Joseph, Jones, Phillip H.
Publikováno v:
In Journal of Systems Architecture February 2021 113
