no code implementations • 14 Dec 2023 • Audrey Chung, Francis Li, Jeremy Ward, Andrew Hryniowski, Alexander Wong
In this paper, we present the DarwinAI Visual Quality Inspection (DVQI) system, a hardware-integration artificial intelligence system for the automated inspection of printed circuit board assembly defects in an electronics manufacturing environment.
no code implementations • 23 Jan 2023 • Brian Li, Steven Palayew, Francis Li, Saad Abbasi, Saeejith Nair, Alexander Wong
There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time-consuming and prone to error, especially at scale.
no code implementations • 29 Nov 2021 • Mohammad Javad Shafiee, Mahmoud Famouri, Gautam Bathla, Francis Li, Alexander Wong
A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws.
no code implementations • 15 Oct 2019 • Mohammad Javad Shafiee, Andrew Hryniowski, Francis Li, Zhong Qiu Lin, Alexander Wong
A particularly interesting class of compact architecture search algorithms are those that are guided by baseline network architectures.
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.
no code implementations • 12 Sep 2019 • Mohammad Javad Shafiee, Mirko Nentwig, Yohannes Kassahun, Francis Li, Stanislav Bochkarev, Akif Kamal, David Dolson, Secil Altintas, Arif Virani, Alexander Wong
The findings of this case study showed that GenSynth is easy to use and can be effective at accelerating the design and production of compact, customized deep neural 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.
1 code implementation • 18 Sep 2017 • Mohammad Javad Shafiee, Brendan Chywl, Francis Li, Alexander Wong
Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene.
no code implementations • 1 Jul 2017 • Mohammad Javad Shafiee, Francis Li, Alexander Wong
A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increase the size and depth of deep neural networks, leading to significant improvements in modeling accuracy.