About Me

Hi! I am a rising fifth-year Computer Science PhD student at University of California, Berkeley, advised by Chris Fletcher. I work on understanding efficient implementations of domain-specific kernels with a focus on building abstractions that unify a wide variety of kernels and accelerator designs into a small set of primitives, in collaboration with Joel Emer and Michael Pellauer. I have applied this analysis to a range of domains, including sparse tensor algebra, transformers, and fully homomorphic encryption.

I transferred to UC Berkeley in January 2024 following my advisor, before which, I was a student at University of Illinois Urbana-Champaign. There, I worked on hardware security and began my research on domain-specific kernels.

Before coming to the University of Illinois, I completed my B.S. in Computer Science from Harvey Mudd College in 2020. There, I worked with Chris Clark in the Lab for Autonomous and Intelligent Robotics. Additionally, for my senior capstone project, I added a numerical programming library to the programming language Factor.

In my free time, I enjoy cooking, social dancing, traveling with my family, and studying Korean.

Please feel free to reach out to me by email at nandeeka [at] berkeley [dot] edu, on GitHub, or on LinkedIn.

Recent Publications

Nandeeka Nayak, Xinrui Wu, Toluwanimi O. Odemuyiwa, Michael Pellauer, Joel S. Emer, and Christopher W. Fletcher. “FuseMax: Leveraging Extended Einsums to Optimize Attention Accelerator Design”. In submission.

Nandeeka Nayak, Toluwanimi O. Odemuyiwa, Shubham Ugare, Christopher W. Fletcher, Michael Pellauer, and Joel S. Emer. “TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators”. MICRO ’23. [paper]
IEEE Micro Top Picks 2023 Honorable Mention

Jose Rodrigo Sanchez Vicarte, Pradyumna Shome, Nandeeka Nayak, Caroline Trippel, Adam Morrison, David Kohlbrenner, and Christopher W. Fletcher. “Opening Pandora’s Box: A Systematic Study of New Ways Microarchitecture Can Leak Private Data”. ISCA ‘21. [paper]
Intel Hardware Security Academic Award 2022 Honorable Mention

Nandeeka Nayak, Makoto Nara, Timmy Gambin, Zoë Wood, and Christopher M. Clark. “Machine learning techniques for auv side-scan sonar data feature extraction as applied to intelligent search for underwater archaeological sites”. FSR ‘19. [paper]

Recent Talks

Extended Einsums: Domain-Specific Kernels in the Language of Tensor Algebra. Stanford AHA Seminar 2024.

TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators. Workshop on Sparse Tensor Computations 2023. [program] [talk]

TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators. CTSTA 2023. [program]

TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators. DRAGSTERS 2023. [program]