Accelerating Random Forests with In-Memory Computing

Interested in artificial intelligence, computer architecture, or hardware design? We are seeking motivated undergraduate students to join a research project exploring next-generation hardware accelerators for Random Forests, one of the most widely used machine learning models for tabular data.

The project investigates how in-memory computing can dramatically improve the speed and energy efficiency of Random Forest inference by performing computation directly inside memory arrays, reducing costly data movement. Students will explore innovative hardware architectures, develop simulation tools, and evaluate new ideas for accelerating decision-tree inference. 

Students will gain experience in:

• Machine learning algorithms (Decision Trees and Random Forests)
• Computer architecture and hardware acceleration
• In-memory computing and emerging memory technologies
• Research methodology and experimental evaluation

Students with backgrounds in Computer Engineering, Electrical Engineering, or Computer Science are encouraged to apply. Experience with Python or C/C++ is required. 

This is an excellent opportunity to participate in cutting-edge research at the intersection of AI and computer architecture, with the potential to contribute to conference publications.