Intel Labs AI Reference Kits for Next-Gen Health Tech

AI Reference Kits Features

With a concentration on AI/ML research, Dev Aryan Khanna is an Intel Student Ambassador for oneAPI. His most recent project was the Healthcare AI Reference Kits Companion, an intelligent healthcare application that combines text-based symptom analysis and image classification with cutting-edge deep learning models. The application aids in the early diagnosis of illnesses using X-ray pictures and symptom descriptions. For model optimization, it makes use of oneAPI libraries and Intel AI Reference Kits.

Concerning the Healthcare AI Companion Initiative

The intrinsic constraint of current healthcare solutions designed exclusively for particular GPUs served as the driving force behind the creation of the Healthcare AI Reference Kits Companion. This was a serious issue since it made it more difficult to adjust to Intel CPUs, which affected the customer base worldwide. By creating an intelligent healthcare tool that is designed for a variety of architectures and focuses on achieving effective performance on Intel hardware, the project seeks to close this gap.

In order to construct the Healthcare AI Reference Kits Companion, oneAPI tools with cross-architecture were utilized. With the use of these tools, developers may create single-language and platform apps that are easily adapted to and optimized for a variety of architectures, including CPUs from Intel. These instruments guarantee flexibility, effectiveness, and ease of use, which is consistent with the project’s objective of offering a complete and adjustable healthcare solution.

Use of Intel Software and Hardware Optimizations

1. Intel Deep Neural Network (oneDNN) Library via oneAPI

How: Showcase how oneDNN may increase CNN layers’ performance for image-based illness identification.

What: Convolutional layers are optimized by the library, which is essential for improving the effectiveness of illness identification in X-ray pictures.

When: During the training and inference phases of the project life cycle, in particular.

2. The Data Analytics Library (oneDAL) of Intel oneAPI

How: OneDAL was used to expedite feature engineering operations inside the data preprocessing pipeline.

What: Although not the main goal, oneDAL helps to improve feature engineering and guarantee data quality, which are essential for precise disease detection models.

When: Just before the model is trained, during the data preparation stage.

3. PyTorch Intel Extension

How: The comparison graph shows how the Intel Extension for PyTorch was able to accelerate model training. Mixed-precision training is also made possible by the PyTorch optimizations.

What: The Intel Extension for PyTorch allows for mixed-precision training without compromising accuracy, optimizes model training, and speeds up deep learning workloads on Intel CPUs.

when: Mainly while the model is being trained.

The Intel Student Ambassador’s Success Story

Indian undergraduate Dev Aryan Kanna attends Guru Gobind Singh Indraprastha University. His experience as an Intel Student Ambassador for oneAPI has been enlightening. As part of the Intel Student Ambassador Program, he has organized workshops, hackathons, and worked on projects that have improved his own learning and given the student attendees invaluable practical experience. With the program, he has access to a wealth of resources, including the newest Intel hardware, which has enhanced his knowledge of Intel technologies and stoked his love for creative problem-solving.

His ambition to use technology to better healthcare has been inspired by the Healthcare AI Reference Kits Companion project, which placed first runner-up in a recent Intel Student Ambassador Hackathon. He hopes to continue making a significant contribution to the nexus of artificial intelligence and healthcare with continued access to Intel’s capabilities.