72 citations · 73 across the 4 of their papers we have counts for
5 papers
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
Habib Hajimolahoseini, Omar Mohamed Awad, Walid Ahmed +8
In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of…
CARLA: A Convolution Accelerator with a Reconfigurable and Low-Energy Architecture
Mehdi Ahmadi, Shervin Vakili, J. M. Pierre Langlois
Convolutional Neural Networks (CNNs) have proven to be extremely accurate for image recognition, even outperforming human recognition capability. When deployed on battery-powered m…
An Energy-Efficient Accelerator Architecture with Serial Accumulation Dataflow for Deep CNNs
Mehdi Ahmadi, Shervin Vakili, J. M. Pierre Langlois
Convolutional Neural Networks (CNNs) have shown outstanding accuracy for many vision tasks during recent years. When deploying CNNs on portable devices and embedded systems, howeve…
Reproducing AmbientGAN: Generative models from lossy measurements
Mehdi Ahmadi, Timothy Nest, Mostafa Abdelnaim +1
In recent years, Generative Adversarial Networks (GANs) have shown substantial progress in modeling complex distributions of data. These networks have received tremendous attention…
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images
Zahra Sobhaninia, Safiyeh Rezaei, Alireza Noroozi +5
Recently deep learning has been playing a major role in the field of computer vision. One of its applications is the reduction of human judgment in the diagnosis of diseases. Espec…