Mohd Nazri Mohd Warip
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Preferred name
Mohd Nazri Mohd Warip
Official Name
Mohd Nazri , Mohd Warip
Alternative Name
Mohd Warip, Mohd Nazri
Mohd Warip, M. N.
Warip, M. N.
Warip, Mohd Nazri Mohd
Warip, Mohd Nazri bin Mohd
Main Affiliation
Scopus Author ID
36555091900
Researcher ID
EDT-8958-2022

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Deep-Learning Assisting Cerebral Palsy Patient Handgrip Task Translation

2021-07-26 , Fazrul Faiz Zakaria , Mohd Nazri Mohd Warip , Phaklen Ehkan , Muslim Mustapa , Mohd Zaizu Ilyas

An electro-encephalography (EEG) brain-computer interface (BCI) can provide the brain and external environment with separate information sharing and control networks. EEG impulses, though, come from many electrodes, which produce different characteristics, and how the electrodes and features to enhance classification efficiency have been chosen has become an urgent concern. This paper explores the deep convolutional neural network architecture (CNN) hyper-parameters with separating temporal and spatial filters without any pre-processing or artificial extraction processes. It selects the raw EEG signal of electrode pairs over the cortical area as hybrid samples. Our proposed deep-learning model outperforms other neural network models previously applied to this dataset in training time (∼40%) and accuracy (∼6%). Besides, considerations such as optimum order for EEG channels do not limit our model, and it is patient-invariant. The impact of network architecture on decoder output and training time is further discussed.

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Breast cancer classification using deep learning and FPGA inferencing

2023-02-21 , Wong E.H. , Fazrul Faiz Zakaria , Mustapa M. , Mohd Nazri Mohd Warip , Phak Len Al Eh Kan

Implementing deep learning technology with FPGA as an accelerator has become a popular application due to its efficiency and performance. However, given the tremendous data generated on medical diagnosis, normal inference speed is not sufficient. Hence, the FPGA technology is implemented for fast inference. In this context, the FPGA accelerates the deep learning inference process for fast breast cancer classification with minimal latency on real-time deployment. This paper summarizes the findings of model deployment across various computing devices in deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference.

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Deep Learning with FPGA: Age and Gender Recognition for Smart Advertisement Board

2023-10-06 , Yeoh W.S. , Fazrul Faiz Zakaria , Mustapa M. , Mohd Nazri Mohd Warip , Phak Len Al Eh Kan , Mozi A.M.

Age and gender recognition are helpful in various applications, especially in the field of advertising. To replace the traditional advertising method that can only display the same contents to all audiences, a smart advertisement board capable of detecting age and gender of audiences to display relevant contents is required to increase the effectiveness of advertising. This paper will use two image datasets to train and test the Convolutional Neural Network (CNN) based architecture models for age and gender recognition using deep learning. The dataset that produced the best performing model will be implemented on three different devices to observe the performance of the models on each device. A gender recognition model with accuracy of 91.53% and age recognition model with accuracy of 59.62% is produced. The results have also shown the use of Field Programmable Gated Array (FPGA) has greatly boosted the performance of the models in terms of throughput and latency.

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Tuberculosis Classification Using Deep Learning and FPGA Inferencing

2023-02-01 , Fazrul Faiz Zakaria , Asral Bahari Jambek , Norfadila Mahrom , Rafikha Aliana A Raof , Mohd Nazri Mohd Warip , Al Eh Kan P.L. , Mustapa M.

Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.

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