260 citations · 300 across the 7 of their papers we have counts for
8 papers
SlimFit: Memory-Efficient Fine-Tuning of Transformer-based Models Using Training Dynamics
Arash Ardakani, Altan Haan, Shangyin Tan +4
Transformer-based models, such as BERT and ViT, have achieved state-of-the-art results across different natural language processing (NLP) and computer vision (CV) tasks. However, t…
Standard Deviation-Based Quantization for Deep Neural Networks
Amir Ardakani, Arash Ardakani, Brett Meyer +2
Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices. Inspired by ex…
Learning to Skip Ineffectual Recurrent Computations in LSTMs
Arash Ardakani, Zhengyun Ji, Warren J. Gross
Long Short-Term Memory (LSTM) is a special class of recurrent neural network, which has shown remarkable successes in processing sequential data. The typical architecture of an LST…
Learning Recurrent Binary/Ternary Weights
Arash Ardakani, Zhengyun Ji, Sean C. Smithson +2
Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. Th…
Design and Implementation of a Polar Codes Blind Detection Scheme
Carlo Condo, Seyyed Ali Hashemi, Arash Ardakani +2
In blind detection, a set of candidates has to be decoded within a strict time constraint, to identify which transmissions are directed at the user equipment. Blind detection is re…
Multi-Mode Inference Engine for Convolutional Neural Networks
Arash Ardakani, Carlo Condo, Warren J. Gross
During the past few years, interest in convolutional neural networks (CNNs) has risen constantly, thanks to their excellent performance on a wide range of recognition and classific…
Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks
Arash Ardakani, Carlo Condo, Warren J. Gross
Recently deep neural networks have received considerable attention due to their ability to extract and represent high-level abstractions in data sets. Deep neural networks such as…
VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing
Arash Ardakani, François Leduc-Primeau, Naoya Onizawa +2
The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware…