Kartik Hegde

Research Intern at NVIDIA - CS PhD Student at UIUC

Kartik Hegde is a notable computer architect and researcher, currently serving as a Research Intern at Facebook AI. He has a strong academic background, having completed his Ph.D. in Computer Science at the University of Illinois at Urbana-Champaign (UIUC), where he was advised by Professor Chris Fletcher. His research focuses on the intersection of computer architecture and machine learning, particularly in designing high-performance processors tailored for various machine learning workloads.1 2

Academic and Professional Background

Education:
- Ph.D. in Computer Science, UIUC (2017-2022)
- B.Tech in Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal (2011-2015)
Research Interests:
- Development of efficient processors for machine learning applications.
- Creation of hardware accelerators for deep learning and other computational tasks.
Notable Achievements:
- Awarded the Facebook PhD Fellowship in 2019 for his work on hardware and software infrastructures for machine learning.1 2
- Recognized for his contributions to the field with multiple publications and citations.3

Current Role

As a Research Intern at Facebook AI, Hegde has been involved in developing novel control paradigms for hardware accelerators aimed at optimizing performance in irregular computing tasks. His work is pivotal in advancing the capabilities of mobile computing by enabling more efficient processing directly on devices rather than relying solely on cloud computing.2 4

Future Endeavors

Following his internship, Hegde continues to explore opportunities that bridge the gap between hardware design and machine learning applications. He is also engaged as an entrepreneur-in-residence at the Allen Institute for AI, where he focuses on innovative chip design methodologies.2 4

Kartik Hegde's contributions to the field of computer architecture and machine learning position him as a significant figure in advancing technology that integrates deep learning into everyday devices.