most citedFathom: Reference Workloads for Modern Deep Learning Methods

182 citations · 186 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CR2024

Osiris: A Systolic Approach to Accelerating Fully Homomorphic Encryption

Austin Ebel, Brandon Reagen

In this paper we show how fully homomorphic encryption (FHE) can be accelerated using a systolic architecture. We begin by analyzing FHE algorithms and then develop systolic or sys…

cs.AR202418 cited

SZKP: A Scalable Accelerator Architecture for Zero-Knowledge Proofs

Alhad Daftardar, Brandon Reagen, Siddharth Garg

Zero-Knowledge Proofs (ZKPs) are an emergent paradigm in verifiable computing. In the context of applications like cloud computing, ZKPs can be used by a client (called the verifie…

cs.CR2023

PriViT: Vision Transformers for Fast Private Inference

Naren Dhyani, Jianqiao Mo, Minsu Cho +4

The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for…

cs.CR20231 cited

Privacy Preserving In-memory Computing Engine

Haoran Geng, Jianqiao Mo, Dayane Reis +5

Privacy has rapidly become a major concern/design consideration. Homomorphic Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support computations o…

cs.CR20234 cited

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.…

cs.AR2023

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…