The goal of this work is to find out the impact of approximated computing on accuracy of deep neural network, specifically neural networks for image classification. A version of framework Caffe called Ristretto-caffe was chosen for neural network implementation, which was extended for the use of approximated operations. Approximated computing was used for multiplication in forward pass for convolution. Approximated components from Evoapproxlib were chosen for this work
Approximate computation is a new trend that explores and harnesses trade-offs between the precision ...
<p>This tutorial investigates various tools for designing Deep Learning Neural Networks (DLNN). Our ...
International audienceThe design and implementation of Deep Learning (DL) models is currently receiv...
Cílem mé práce je zjistit vliv a dopad aproximovaného počítání na přesnost hluboké neuronové sítě, k...
Neural networks are currently state-of-the-art technology for speech, image and other recognition ta...
Recently, deep learning is at the forefront of the state-of-the-art machine learning algorithms and ...
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks....
International audienceThe design and implementation of Convolutional Neural Networks (CNNs) for deep...
Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.Includes bibliographical references (leaves 144...
This paper investigates about the possibility to reduce power consumption in Neural Network using ap...
Artificial neural networks (ANNs) are a class of machine learning models that are loosely inspired b...
The aim of this thesis was to create a program for visualization of artificial neural networks. The ...
This is a small code that helps test the function approximation by dense neural networks. The co...
This article analyzes the effects of approximate multiplication when performing inferences on deep c...
This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs)...
Approximate computation is a new trend that explores and harnesses trade-offs between the precision ...
<p>This tutorial investigates various tools for designing Deep Learning Neural Networks (DLNN). Our ...
International audienceThe design and implementation of Deep Learning (DL) models is currently receiv...
Cílem mé práce je zjistit vliv a dopad aproximovaného počítání na přesnost hluboké neuronové sítě, k...
Neural networks are currently state-of-the-art technology for speech, image and other recognition ta...
Recently, deep learning is at the forefront of the state-of-the-art machine learning algorithms and ...
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks....
International audienceThe design and implementation of Convolutional Neural Networks (CNNs) for deep...
Thesis (Ph. D.)--University of Hawaii at Manoa, 1992.Includes bibliographical references (leaves 144...
This paper investigates about the possibility to reduce power consumption in Neural Network using ap...
Artificial neural networks (ANNs) are a class of machine learning models that are loosely inspired b...
The aim of this thesis was to create a program for visualization of artificial neural networks. The ...
This is a small code that helps test the function approximation by dense neural networks. The co...
This article analyzes the effects of approximate multiplication when performing inferences on deep c...
This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs)...
Approximate computation is a new trend that explores and harnesses trade-offs between the precision ...
<p>This tutorial investigates various tools for designing Deep Learning Neural Networks (DLNN). Our ...
International audienceThe design and implementation of Deep Learning (DL) models is currently receiv...