Publication date
July 2021
DOIAbstract
This is the source code for the SC'21 paper: APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Core
This is the source code for the SC'21 paper: APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Core
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Artifact for accepted OSDI'23 paper, Yuke Wang, et al. MGG: Accelerating Graph Neural Networks with ...
Convolutional neural networks (CNNs) have been extensively used in many aspects, such as face and sp...
Artifact for USENIX ATC'23: TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
This is the source code for Feed Forward Neural Network with Random Quaternionic Neurons. <br
none5noopenGarofalo, Angelo; Tagliavini, Giuseppe; Conti, Francesco; Rossi, Davide; Benini, LucaGaro...
The current deep learning application scenario is more and more extensive. In terms of computing pla...
This repository contains code material for the publication: Stanojevic, A., Woźniak, S., Bellec, G.,...
The code of the manuscript submission "Training large-scale optoelectronic neural networks with dual...
This is the code release of the information maximising neural network code used in the paper Automat...
Contains fulltext : 191773.pdf (publisher's version ) (Closed access
This thesis deals with a training of the FPNN structures. It focuses on the ways of direct conversio...
Code for the publication V. Liakoni*, A. Modirshanechi, W. Gerstner, J. Brea, Neural Computation, 20...
work [1,2] was introduced in 2011 to facilitate the efficient use of graphical processing units (GPU...
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. The...
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Artifact for accepted OSDI'23 paper, Yuke Wang, et al. MGG: Accelerating Graph Neural Networks with ...
Convolutional neural networks (CNNs) have been extensively used in many aspects, such as face and sp...
Artifact for USENIX ATC'23: TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
This is the source code for Feed Forward Neural Network with Random Quaternionic Neurons. <br
none5noopenGarofalo, Angelo; Tagliavini, Giuseppe; Conti, Francesco; Rossi, Davide; Benini, LucaGaro...
The current deep learning application scenario is more and more extensive. In terms of computing pla...
This repository contains code material for the publication: Stanojevic, A., Woźniak, S., Bellec, G.,...
The code of the manuscript submission "Training large-scale optoelectronic neural networks with dual...
This is the code release of the information maximising neural network code used in the paper Automat...
Contains fulltext : 191773.pdf (publisher's version ) (Closed access
This thesis deals with a training of the FPNN structures. It focuses on the ways of direct conversio...
Code for the publication V. Liakoni*, A. Modirshanechi, W. Gerstner, J. Brea, Neural Computation, 20...
work [1,2] was introduced in 2011 to facilitate the efficient use of graphical processing units (GPU...
Compiler frameworks are crucial for the widespread use of FPGA-based deep learning accelerators. The...
In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, e...
Artifact for accepted OSDI'23 paper, Yuke Wang, et al. MGG: Accelerating Graph Neural Networks with ...
Convolutional neural networks (CNNs) have been extensively used in many aspects, such as face and sp...
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