4 citations · 8 across the 5 of their papers we have counts for
5 papers
Network and Compiler Optimizations for Efficient Linear Algebra Kernels in Private Transformer Inference
Karthik Garimella, Negar Neda, Austin Ebel +2
Large language model (LLM) based services are primarily structured as client-server interactions, with clients sending queries directly to cloud providers that host LLMs. This appr…
CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption
Negar Neda, Austin Ebel, Benedict Reynwar +1
Homomorphic encryption (HE) is a privacy-preserving computation technique that enables computation on encrypted data. Today, the potential of HE remains largely unrealized as it is…
Towards Fast and Scalable Private Inference
Jianqiao Mo, Karthik Garimella, Negar Neda +2
Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is use…
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi +21
Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed.…
RPU: The Ring Processing Unit
Deepraj Soni, Negar Neda, Naifeng Zhang +14
Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cr…