Add to ORKGImage recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Failure in real-time image recognition tasks can occur due to incorrect mapping on hardware accelerators, which may lead to timing uncertainty and incorrect behavior. Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment as parameters like deep learning frameworks, compiler optimizations for code generation, and hardware devices are not regulated with varying impact on model performance and correctness. ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
We propose a novel method for training a neural network for image classification to reduce input dat...
DNNs have been finding a growing number of applications including image classification, speech recog...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed ...
Deep learning is overhauling a plethora of applications such as voice assistants, autonomous vehicle...
While providing the same functionality, the various Deep Learning software frameworks available thes...
In the rapidly growing field of artificial intelligence (AI), machine vision is an important area wi...
In this era, machine learning and deep learning has become very ubiquitous and dominant in our socie...
There exist numerous scientific contributions to the design of deep learning networks. However, usin...
Resizing images is a critical pre-processing step in computer vision. Principally, deep learning mod...
We devise a performance model for GPU training of Deep Learning Recommendation Models (DLRM), whose ...
Deep learning's recent history has been one of achievement: from triumphing over humans in the game ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
We propose a novel method for training a neural network for image classification to reduce input dat...
DNNs have been finding a growing number of applications including image classification, speech recog...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
Image recognition tasks typically use deep learning and require enormous processing power, thus rely...
This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed ...
Deep learning is overhauling a plethora of applications such as voice assistants, autonomous vehicle...
While providing the same functionality, the various Deep Learning software frameworks available thes...
In the rapidly growing field of artificial intelligence (AI), machine vision is an important area wi...
In this era, machine learning and deep learning has become very ubiquitous and dominant in our socie...
There exist numerous scientific contributions to the design of deep learning networks. However, usin...
Resizing images is a critical pre-processing step in computer vision. Principally, deep learning mod...
We devise a performance model for GPU training of Deep Learning Recommendation Models (DLRM), whose ...
Deep learning's recent history has been one of achievement: from triumphing over humans in the game ...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
We propose a novel method for training a neural network for image classification to reduce input dat...
DNNs have been finding a growing number of applications including image classification, speech recog...