A survey on efficient convolutional neural networks and hardware acceleration

D Ghimire, D Kil, S Kim - Electronics, 2022‏ - mdpi.com
Over the past decade, deep-learning-based representations have demonstrated remarkable
performance in academia and industry. The learning capability of convolutional neural …

Structured pruning for deep convolutional neural networks: A survey

Y He, L **ao - IEEE transactions on pattern analysis and …, 2023‏ - ieeexplore.ieee.org
The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

Model compression and hardware acceleration for neural networks: A comprehensive survey

L Deng, G Li, S Han, L Shi, Y **e - Proceedings of the IEEE, 2020‏ - ieeexplore.ieee.org
Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Speech recognition using deep neural networks: A systematic review

AB Nassif, I Shahin, I Attili, M Azzeh, K Shaalan - IEEE access, 2019‏ - ieeexplore.ieee.org
Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

A survey on green deep learning

J Xu, W Zhou, Z Fu, H Zhou, L Li - arxiv preprint arxiv:2111.05193, 2021‏ - arxiv.org
In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …

Mcdnn: An approximation-based execution framework for deep stream processing under resource constraints

S Han, H Shen, M Philipose, S Agarwal… - Proceedings of the 14th …, 2016‏ - dl.acm.org
We consider applying computer vision to video on cloud-backed mobile devices using Deep
Neural Networks (DNNs). The computational demands of DNNs are high enough that …

Fast, lean, and accurate: Modeling password guessability using neural networks

W Melicher, B Ur, SM Segreti, S Komanduri… - 25th USENIX Security …, 2016‏ - usenix.org
Human-chosen text passwords, today's dominant form of authentication, are vulnerable to
guessing attacks. Unfortunately, existing approaches for evaluating password strength by …

Transfer learning for speech and language processing

D Wang, TF Zheng - 2015 Asia-Pacific Signal and Information …, 2015‏ - ieeexplore.ieee.org
Transfer learning is a vital technique that generalizes models trained for one setting or task
to other settings or tasks. For example in speech recognition, an acoustic model trained for …

An overview of neural network compression

JO Neill - arxiv preprint arxiv:2006.03669, 2020‏ - arxiv.org
Overparameterized networks trained to convergence have shown impressive performance
in domains such as computer vision and natural language processing. Pushing state of the …

Recent progresses in deep learning based acoustic models

D Yu, J Li - IEEE/CAA Journal of automatica sinica, 2017‏ - ieeexplore.ieee.org
In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …