Enviar pesquisa
Carregar
DOC836
•
0 gostou
•
240 visualizações
N
Nada Nagi
Seguir
Denunciar
Compartilhar
Denunciar
Compartilhar
1 de 1
Baixar agora
Baixar para ler offline
Recomendados
Funciones Y Operadores De Excel
Funciones Y Operadores De Excel
karen14
penjelasan mengenai alasan penggunaan tegangan DC pada laptop
Tugas1 sistem.mikroprosesor(aprilia 1410501019)
Tugas1 sistem.mikroprosesor(aprilia 1410501019)
Aprilia Ningsih
This is a brief outline of .Net study for week 1 and week 2. For further details will be out soon.
MOKA .Net Study Outline
MOKA .Net Study Outline
Aliencube Consulting
Coevaluación
Ficha coevaluacion trabajo final
Ficha coevaluacion trabajo final
Miguel Angel Pallete
Book Halong Bay Cruises at best price from asiapearltravel.com. We are offering great deals or discounts on bay cruises tour packages in Halong bay.
Vietnam Shore Excursions
Vietnam Shore Excursions
halongcruisesluxury
.
Exit poll analysis
Exit poll analysis
Mia Buckley
Zarządzanie komunikacją
Zarządzanie komunikacją
Justyna Wójcik
Tulipp: Towards Ubiquitous Low-power Image Processing Platforms
Tulipp_H2020_Hipeac'17 Conference_PEPGUM Workshop_January 017
Tulipp_H2020_Hipeac'17 Conference_PEPGUM Workshop_January 017
Tulipp. Eu
Recomendados
Funciones Y Operadores De Excel
Funciones Y Operadores De Excel
karen14
penjelasan mengenai alasan penggunaan tegangan DC pada laptop
Tugas1 sistem.mikroprosesor(aprilia 1410501019)
Tugas1 sistem.mikroprosesor(aprilia 1410501019)
Aprilia Ningsih
This is a brief outline of .Net study for week 1 and week 2. For further details will be out soon.
MOKA .Net Study Outline
MOKA .Net Study Outline
Aliencube Consulting
Coevaluación
Ficha coevaluacion trabajo final
Ficha coevaluacion trabajo final
Miguel Angel Pallete
Book Halong Bay Cruises at best price from asiapearltravel.com. We are offering great deals or discounts on bay cruises tour packages in Halong bay.
Vietnam Shore Excursions
Vietnam Shore Excursions
halongcruisesluxury
.
Exit poll analysis
Exit poll analysis
Mia Buckley
Zarządzanie komunikacją
Zarządzanie komunikacją
Justyna Wójcik
Tulipp: Towards Ubiquitous Low-power Image Processing Platforms
Tulipp_H2020_Hipeac'17 Conference_PEPGUM Workshop_January 017
Tulipp_H2020_Hipeac'17 Conference_PEPGUM Workshop_January 017
Tulipp. Eu
DOC443
DOC443
Nada Nagi
DOC438
DOC438
Nada Nagi
Project Myanmar 280515
Project Myanmar 280515
Olaitan Awonusi
DOC439
DOC439
Nada Nagi
Qui a compris les proba, les stats? Peu parmi nous. Pourtant tout le monde parle de ML et DL Nouvel croyance?
Fondement et biais du Machine Learning et du Deep Learning
Fondement et biais du Machine Learning et du Deep Learning
Richard Pawlowsky
For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/arm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-iodice For more information about embedded vision, please visit: http://www.embedded-vision.com Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit. Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation, lodice discusses and analyzes how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNN) implementations. After a brief introduction to the nature of CNN computations, Iodice explores the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. He shows examples of OpenCL implementations of these functions and highlights their advantages, limitations and trade-offs. Central to the techniques explored is an emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.
"Using SGEMM and FFTs to Accelerate Deep Learning," a Presentation from ARM
"Using SGEMM and FFTs to Accelerate Deep Learning," a Presentation from ARM
Edge AI and Vision Alliance
Mais conteúdo relacionado
Destaque
DOC443
DOC443
Nada Nagi
DOC438
DOC438
Nada Nagi
Project Myanmar 280515
Project Myanmar 280515
Olaitan Awonusi
DOC439
DOC439
Nada Nagi
Qui a compris les proba, les stats? Peu parmi nous. Pourtant tout le monde parle de ML et DL Nouvel croyance?
Fondement et biais du Machine Learning et du Deep Learning
Fondement et biais du Machine Learning et du Deep Learning
Richard Pawlowsky
For the full video of this presentation, please visit: http://www.embedded-vision.com/platinum-members/arm/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit-iodice For more information about embedded vision, please visit: http://www.embedded-vision.com Gian Marco Iodice, Software Engineer at ARM, presents the "Using SGEMM and FFTs to Accelerate Deep Learning" tutorial at the May 2016 Embedded Vision Summit. Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning, they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation, lodice discusses and analyzes how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNN) implementations. After a brief introduction to the nature of CNN computations, Iodice explores the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. He shows examples of OpenCL implementations of these functions and highlights their advantages, limitations and trade-offs. Central to the techniques explored is an emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.
"Using SGEMM and FFTs to Accelerate Deep Learning," a Presentation from ARM
"Using SGEMM and FFTs to Accelerate Deep Learning," a Presentation from ARM
Edge AI and Vision Alliance
Destaque
(6)
DOC443
DOC443
DOC438
DOC438
Project Myanmar 280515
Project Myanmar 280515
DOC439
DOC439
Fondement et biais du Machine Learning et du Deep Learning
Fondement et biais du Machine Learning et du Deep Learning
"Using SGEMM and FFTs to Accelerate Deep Learning," a Presentation from ARM
"Using SGEMM and FFTs to Accelerate Deep Learning," a Presentation from ARM
Baixar agora