71 citations · 203 across the 12 of their papers we have counts for
12 papers
DVQI: A Multi-task, Hardware-integrated Artificial Intelligence System for Automated Visual Inspection in Electronics Manufacturing
Audrey Chung, Francis Li, Jeremy Ward +2
As electronics manufacturers continue to face pressure to increase production efficiency amid difficulties with supply chains and labour shortages, many printed circuit board assem…
PCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge
Brian Li, Steven Palayew, Francis Li +3
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. Ther…
TinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection
Mohammad Javad Shafiee, Mahmoud Famouri, Gautam Bathla +2
A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws. Human-only visual inspection can be very time-cons…
State of Compact Architecture Search For Deep Neural Networks
Mohammad Javad Shafiee, Andrew Hryniowski, Francis Li +2
The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge and mobile scenarios. Du…
YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection
Alexander Wong, Mahmoud Famuori, Mohammad Javad Shafiee +3
Object detection remains an active area of research in the field of computer vision, and considerable advances and successes has been achieved in this area through the design of de…
Human-Machine Collaborative Design for Accelerated Design of Compact Deep Neural Networks for Autonomous Driving
Mohammad Javad Shafiee, Mirko Nentwig, Yohannes Kassahun +7
An effective deep learning development process is critical for widespread industrial adoption, particularly in the automotive sector. A typical industrial deep learning development…
FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis
Alexander Wong, Mohammad Javad Shafiee, Brendan Chwyl +1
The tremendous potential exhibited by deep learning is often offset by architectural and computational complexity, making widespread deployment a challenge for edge scenarios such…
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
Alexander Wong, Mohammad Javad Shafiee, Francis Li +1
Object detection is a major challenge in computer vision, involving both object classification and object localization within a scene. While deep neural networks have been shown in…
StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks
Mohammad Javad Shafiee, Brendan Chwyl, Francis Li +4
The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption, particularly for us…
SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis
Mohammad Javad Shafiee, Francis Li, Brendan Chwyl +1
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…
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Mohammad Javad Shafiee, Brendan Chywl, Francis Li +1
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 localizatio…
Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks
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 increa…