Search Results for author: Saad Abbasi

Found 14 papers, 1 papers with code

TurboViT: Generating Fast Vision Transformers via Generative Architecture Search

no code implementations22 Aug 2023 Alexander Wong, Saad Abbasi, Saeejith Nair

In this study, we explore the generation of fast vision transformer architecture designs via generative architecture search (GAS) to achieve a strong balance between accuracy and architectural and computational efficiency.

Computational Efficiency

Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge

no code implementations21 Apr 2023 Alexander Wong, Yifan Wu, Saad Abbasi, Saeejith Nair, Yuhao Chen, Mohammad Javad Shafiee

As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments.

Multi-Task Learning Robotic Grasping

PCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge

no code implementations23 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.

Edge-computing object-detection +1

COVID-Net Assistant: A Deep Learning-Driven Virtual Assistant for COVID-19 Symptom Prediction and Recommendation

no code implementations22 Nov 2022 Pengyuan Shi, Yuetong Wang, Saad Abbasi, Alexander Wong

As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests.

Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers

no code implementations15 Aug 2022 Alexander Wong, Mohammad Javad Shafiee, Saad Abbasi, Saeejith Nair, Mahmoud Famouri

With the growing adoption of deep learning for on-device TinyML applications, there has been an ever-increasing demand for efficient neural network backbones optimized for the edge.

Efficient Neural Network

MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge

no code implementations25 May 2022 Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee

Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient convolutional neural networks for embedded vision applications.

Neural Architecture Search

MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

no code implementations27 Apr 2022 Saeejith Nair, Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee

Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications.

Efficient Neural Network Neural Architecture Search

MAPLE: Microprocessor A Priori for Latency Estimation

no code implementations30 Nov 2021 Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee

Through this quantitative strategy as the hardware descriptor, MAPLE can generalize to new hardware via a few shot adaptation strategy where with as few as 3 samples it exhibits a 6% improvement over state-of-the-art methods requiring as much as 10 samples.

Domain Adaptation Neural Architecture Search +1

COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow

no code implementations14 Sep 2021 Audrey G. Chung, Maya Pavlova, Hayden Gunraj, Naomi Terhljan, Alexander MacLean, Hossein Aboutalebi, Siddharth Surana, Andy Zhao, Saad Abbasi, Alexander Wong

As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 clinical workflow.

BIG-bench Machine Learning

COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging

1 code implementation5 Aug 2021 Alexander MacLean, Saad Abbasi, Ashkan Ebadi, Andy Zhao, Maya Pavlova, Hayden Gunraj, Pengcheng Xi, Sonny Kohli, Alexander Wong

The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections, thereby allowing for both proper treatment for those individuals as well as action to be taken to prevent further spread of the virus.

Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration

no code implementations8 Jul 2021 Saad Abbasi, Mohammad Javad Shafiee, Ellick Chan, Alexander Wong

In this study, a comprehensive empirical exploration is conducted to investigate the impact of deep neural network architecture design on the degree of inference speedup that can be achieved via hardware-specific acceleration.

Neural Architecture Search

COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images

no code implementations4 May 2021 Hossein Aboutalebi, Saad Abbasi, Mohammad Javad Shafiee, Alexander Wong

The health and socioeconomic difficulties caused by the COVID-19 pandemic continues to cause enormous tensions around the world.

Management

Improving Maximal Safe Brain Tumor Resection with Photoacoustic Remote Sensing Microscopy

no code implementations21 Sep 2020 Benjamin R. Ecclestone, Kevan Bell, Saad Abbasi, Deepak Dinakaran, Frank K. H. van Landeghem, John R. Mackey, Paul Fieguth, Parsin Haji Reza

Images obtained using this technique show comparable quality and contrast to the current standard for histopathological assessment of brain tissues.

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