4 code implementations • 3 Oct 2019 • Alexander Wong, Mahmoud Famuori, Mohammad Javad Shafiee, Francis Li, Brendan Chwyl, Jonathan Chung
As such, there has been growing research interest in the design of efficient deep neural network architectures catered for edge and mobile usage.
1 code implementation • 10 May 2019 • Zhong Qiu Lin, Brendan Chwyl, Alexander Wong
In this study, we introduce EdgeSegNet, a compact deep convolutional neural network for the task of semantic segmentation.
no code implementations • 18 Mar 2019 • Alexander Wong, Zhong Qiu Lin, Brendan Chwyl
Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance-level object segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.
no code implementations • 17 Sep 2018 • Alexander Wong, Mohammad Javad Shafiee, Brendan Chwyl, Francis Li
In this study, we introduce the idea of generative synthesis, which is premised on the intricate interplay between a generator-inquisitor pair that work in tandem to garner insights and learn to generate highly efficient deep neural networks that best satisfies operational requirements.
1 code implementation • 19 Feb 2018 • Alexander Wong, Mohammad Javad Shafiee, Francis Li, Brendan Chwyl
The resulting Tiny SSD possess a model size of 2. 3MB (~26X smaller than Tiny YOLO) while still achieving an mAP of 61. 3% on VOC 2007 (~4. 2% higher than Tiny YOLO).
no code implementations • 16 Jan 2018 • Mohammad Javad Shafiee, Brendan Chwyl, Francis Li, Rongyan Chen, Michelle Karg, Christian Scharfenberger, Alexander Wong
The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption, particularly for use in embedded devices.
no code implementations • 20 Nov 2017 • Mohammad Javad Shafiee, Francis Li, Brendan Chwyl, Alexander Wong
While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements.