… is often limited at some points, especially with regular architecture. In this paper, we propose
an efficient and noncomplex image classification architecture using deep learning based on …

… in saliency detection have utilized deep learning to obtain high-… deep learning framework
for accurate and efficient saliency … The short inference time compared to other deep learning-…

… Also, due to the following considerations, the TPU’s homogeneous architecture is not …
architecture to support both CNN and RNN efficiently. Also, an off-chip access-efficient workload …

… We present a deep learning architecture tailored to tuberculosis diagnosis. With this approach
we reduce the computational and memory requirement significantly, without sacrificing the …

… efficiency in the recognition of thyroid nodules. In this work, we propose a deep learning
architecture, … some state-of-the-art deep learning networks. The experimental results show that …

… final accuracy and energy efficiency of neural architectures in a complex and … to learn more
hardware efficient networks. In this paper, we propose to learn more hardware-efficient deep …

… learning algorithm to learn useful CNN features from Alexnet, VGG16, VGG19, GoogleNet,
ResNet and SqueezeNet CNN architectures … SqueezeNet pretrained architectures were used …

… In this paper, we exploit an efficient convolutional network architecture for detecting any
abnormality caused by COVID-19 through chest radiography images. Experiments were …

… To address this issue, we propose a new, computationally efficient deep learning based
method called hist2RNA to predict the expression of genes using digital images of stained …

… We have implemented the proposed and existing deep learning architectures using Python
with PyTorch deep learning framework. We have performed training and inference on an …