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A comprehensive review of binary neural network
Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …
widely adopted in many real-life applications. Despite their various benefits and potentials …
A systematic literature review on binary neural networks
R Sayed, H Azmi, H Shawkey, AH Khalil… - IEEE Access, 2023 - ieeexplore.ieee.org
This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …
utilizes binary weights and activation function parameters to substitute the full-precision …
AWB-GCN: A graph convolutional network accelerator with runtime workload rebalancing
Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …
video, and audio. In many applications, however, information and their relationships are …
I-GCN: A graph convolutional network accelerator with runtime locality enhancement through islandization
Fpga-based deep learning inference accelerators: Where are we standing?
Recently, artificial intelligence applications have become part of almost all emerging
technologies around us. Neural networks, in particular, have shown significant advantages …
technologies around us. Neural networks, in particular, have shown significant advantages …
Accelerating binarized neural networks via bit-tensor-cores in turing gpus
Despite foreseeing tremendous speedups over conventional deep neural networks, the
performance advantage of binarized neural networks (BNNs) has merely been showcased …
performance advantage of binarized neural networks (BNNs) has merely been showcased …