Karthik Garimella

According to our database, Karthik Garimella authored at least 12 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

HE-LRM: Encrypted Deep Learning Recommendation Models using Fully Homomorphic Encryption.

Karthik Garimella, Austin Ebel, Gabrielle De Micheli, Brandon Reagen

CoRR, June, 2025

EinHops: Einsum Notation for Expressive Homomorphic Operations on RNS-CKKS Tensors.

Karthik Garimella, Austin Ebel, Brandon Reagen

IACR Cryptol. ePrint Arch., 2025

Orion: A Fully Homomorphic Encryption Framework for Deep Learning.

Austin Ebel, Karthik Garimella, Brandon Reagen

Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2025

2024

TruncFormer: Private LLM Inference Using Only Truncations.

Patrick Yubeaton, Jianqiao Mo, Karthik Garimella, Nandan Kumar Jha, Brandon Reagen, Chinmay Hegde, Siddharth Garg

CoRR, 2024

2023

F-LEMMA: Fast Learning-Based Energy Management for Multi-/Many-Core Processors.

An Zou, Yehan Ma, Karthik Garimella, Benjamin Lee, Christopher D. Gill, Xuan Zhang

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., February, 2023

Orion: A Fully Homomorphic Encryption Compiler for Private Deep Neural Network Inference.

Austin Ebel, Karthik Garimella, Brandon Reagen

CoRR, 2023

Towards Fast and Scalable Private Inference.

Jianqiao Mo, Karthik Garimella, Negar Neda, Austin Ebel, Brandon Reagen

Proceedings of the 20th ACM International Conference on Computing Frontiers, 2023

Characterizing and Optimizing End-to-End Systems for Private Inference.

Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2023

2021

CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale.

Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen

CoRR, 2021

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning.

Karthik Garimella, Nandan Kumar Jha, Brandon Reagen

CoRR, 2021

2020

Attacking vision-based perception in end-to-end autonomous driving models.

Adith Boloor, Karthik Garimella, Xin He, Christopher D. Gill, Yevgeniy Vorobeychik, Xuan Zhang

J. Syst. Archit., 2020

F-LEMMA: Fast Learning-based Energy Management for Multi-/Many-core Processors.

An Zou, Karthik Garimella, Benjamin Lee, Christopher D. Gill, Xuan Zhang

Proceedings of the MLCAD '20: 2020 ACM/IEEE Workshop on Machine Learning for CAD, 2020