2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.AR2023
FHEmem: A Processing In-Memory Accelerator for Fully Homomorphic Encryption
Minxuan Zhou, Yujin Nam, Pranav Gangwar +7
Fully Homomorphic Encryption (FHE) is a technique that allows arbitrary computations to be performed on encrypted data without the need for decryption, making it ideal for securing…
cs.CR2022★ 2 cited
MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory
Saransh Gupta, Rosario Cammarota, Tajana Rosing
The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in health…
cs.NE2018
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework
Mohsen Imani, Mohammad Samragh, Yeseong Kim +3
Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-the-art D…