Blockchain based decentralized learning for security in digital twins
Z Lv, C Cheng, H Lv - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
This work aims to analyze malicious communication behaviors that pose a threat to the
security of digital twins (DTs) and safeguard user privacy. A unified and integrated …
security of digital twins (DTs) and safeguard user privacy. A unified and integrated …
[HTML][HTML] Reformulating the direct convolution for high-performance deep learning inference on ARM processors
We present two high-performance implementations of the convolution operator via the direct
algorithm that outperform the so-called lowering approach based on the im2col transform …
algorithm that outperform the so-called lowering approach based on the im2col transform …
[HTML][HTML] Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors
S Barrachina, MF Dolz, P San Juan… - Journal of Parallel and …, 2022 - Elsevier
Abstract Convolutional Neural Networks (CNNs) play a crucial role in many image
recognition and classification tasks, recommender systems, brain-computer interfaces, etc …
recognition and classification tasks, recommender systems, brain-computer interfaces, etc …
[HTML][HTML] Minimization of high computational cost in data preprocessing and modeling using MPI4Py
Data preprocessing is a fundamental stage in deep learning modeling and serves as the
cornerstone of reliable data analytics. These deep learning models require significant …
cornerstone of reliable data analytics. These deep learning models require significant …
Distributed quantum learning with co-management in a multi-tenant quantum system
A D'Onofrio, A Hossain, L Santana… - … Conference on Big …, 2023 - ieeexplore.ieee.org
The rapid advancement of quantum computing has pushed classical designs into the
quantum domain, breaking physical boundaries for computing-intensive and data-hungry …
quantum domain, breaking physical boundaries for computing-intensive and data-hungry …
[HTML][HTML] High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS
We evolve PyDTNN, a framework for distributed parallel training of Deep Neural Networks
(DNNs), into an efficient inference tool for convolutional neural networks. Our optimization …
(DNNs), into an efficient inference tool for convolutional neural networks. Our optimization …
[HTML][HTML] Efficient and portable Winograd convolutions for multi-core processors
We take a step forward towards developing high-performance codes for the convolution
operator, based on the Winograd algorithm, that are easy to customise for general-purpose …
operator, based on the Winograd algorithm, that are easy to customise for general-purpose …
[HTML][HTML] Performance–energy trade-offs of deep learning convolution algorithms on ARM processors
In this work, we assess the performance and energy efficiency of high-performance codes
for the convolution operator, based on the direct, explicit/implicit lowering and Winograd …
for the convolution operator, based on the direct, explicit/implicit lowering and Winograd …
[HTML][HTML] Using machine learning to model the training scalability of convolutional neural networks on clusters of GPUs
In this work, we build a general piece-wise model to analyze data-parallel (DP) training
costs of convolutional neural networks (CNNs) on clusters of GPUs. This general model is …
costs of convolutional neural networks (CNNs) on clusters of GPUs. This general model is …
Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architectures
We present high performance, multi-threaded implementations of three GEMM-based
convolution algorithms for multicore processors with ARM and RISC-V architectures. The …
convolution algorithms for multicore processors with ARM and RISC-V architectures. The …