Intel Multicore Lab
Intel Multicore Lab
Overview
The Intel Multicore Programming Laboratory focuses on understanding and implementing parallel algorithms using multicore processors to improve performance and efficiency. Students learn to develop, analyze, and optimize multithreaded applications using tools and libraries such as OpenMP, Intel oneAPI, and parallel programming models. The lab also integrates foundational concepts of quantum computing with real-world applications of the Internet of Things (IoT), enabling learners to bridge theoretical knowledge with hands-on implementation.
Objectives
- To enhance student employability by developing competencies required for cloud computing, AI, and data analytics roles.
- To facilitate research in parallel algorithms, performance optimization, and scalable computing systems.
- To enable students to analyze program performance, identify bottlenecks, and apply optimization techniques.
- To support outcome-based education by improving problem-solving and analytical skills.
- To promote innovation through project-based learning, hackathons, and product-oriented development.
Key Focus Areas
- Core domains the lab works on: Programming for Data Science, Deep Learning Lab, Machine Vision Lab and Programming in IOT
- Principles of operation of sensors and actuators
- Sensor data collection and visualization
- Proximity and displacement sensors
- Technology areas, frameworks, or experimental fields
On-going Project Titles :
- CBAM-Enhanced Attention U-Net for Automated Pulmonary Disease Detection from Chest X-ray Images.
Research Publications
- Sheng, Xinlei, Chen Wang, Jian Shen, Hemalatha Sattamuthu, and Niranchana Radhakrishnan. “Verifiable Private Data Access Control in Consumer Electronics for Smart Cities.” IEEE Consumer Electronics Magazine (2025).
- Kumar, V. Vinoth, KM Karthick Raghunath, Iyappan Perumal, and K. Manikandan. “Leveraging Personalized Customer Experiences in Mobile Edge Computing Through Split Learning Using Smart Data-Driven Modeling.” IEEE Access (2025).
- Maruthupandi, J., S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, and Shitharth Selvarajan. “An intelligent attention-based deep convoluted learning (IADCL) model for smart healthcare security.” Scientific Reports 15, no. 1 (2025): 1363.
