no code implementations • 21 Nov 2023 • Yanan Jian, Fuxun Yu, Simranjit Singh, Dimitrios Stamoulis
Aerial object detection is a challenging task, in which one major obstacle lies in the limitations of large-scale data collection and the long-tail distribution of certain classes.
no code implementations • 22 Nov 2020 • Fuxun Yu, Dimitrios Stamoulis, Di Wang, Dimitrios Lymberopoulos, Xiang Chen
This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs).
1 code implementation • 1 Jul 2019 • Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu
In this work, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints.
no code implementations • 10 May 2019 • Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the latency constraint of a mobile device?
9 code implementations • 5 Apr 2019 • Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device?
Ranked #891 on Image Classification on ImageNet
no code implementations • 14 Sep 2018 • Diana Marculescu, Dimitrios Stamoulis, Ermao Cai
What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)?
no code implementations • 5 Aug 2018 • Dimitrios Stamoulis, Ting-Wu Chin, Anand Krishnan Prakash, Haocheng Fang, Sribhuvan Sajja, Mitchell Bognar, Diana Marculescu
We cast the design of adaptive CNNs as a hyper-parameter optimization problem with respect to energy, accuracy, and communication constraints imposed by the mobile device.
no code implementations • 6 Dec 2017 • Dimitrios Stamoulis, Ermao Cai, Da-Cheng Juan, Diana Marculescu
While selecting the hyper-parameters of Neural Networks (NNs) has been so far treated as an art, the emergence of more complex, deeper architectures poses increasingly more challenges to designers and Machine Learning (ML) practitioners, especially when power and memory constraints need to be considered.
2 code implementations • 15 Oct 2017 • Ermao Cai, Da-Cheng Juan, Dimitrios Stamoulis, Diana Marculescu
We also propose the "energy-precision ratio" (EPR) metric to guide machine learners in selecting an energy-efficient CNN architecture that better trades off the energy consumption and prediction accuracy.