45 citations · 50 across the 8 of their papers we have counts for
9 papers
GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs
Lara D'Agata, Carlos Agulló-Domingo, Óscar Vera-López +7
Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the latest advances in accelerated…
FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption
Lohit Daksha, Seyda Guzelhan, Kaustubh Shivdikar +10
Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data but incurs massive computational and memory overheads, often exceeding plaintext execution by seve…
Characterizing the Behavior of Training Mamba-based State Space Models on GPUs
Trinayan Baruah, Kaustubh Shivdikar, Sara Prescott +1
Mamba-based State Space Models (SSM) have emerged as a promising alternative to the ubiquitous transformers. Despite the expressive power of transformers, the quadratic complexity…
FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs
Carlos Agulló-Domingo, Óscar Vera-López, Seyda Guzelhan +7
Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving appr…
NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator
Kaustubh Shivdikar, Nicolas Bohm Agostini, Malith Jayaweera +5
Graph Neural Networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatics. Des…
Enabling Accelerators for Graph Computing
Kaustubh Shivdikar
The advent of Graph Neural Networks (GNNs) has revolutionized the field of machine learning, offering a novel paradigm for learning on graph-structured data. Unlike traditional neu…
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training
Hongwu Peng, Xi Xie, Kaustubh Shivdikar +6
In the acceleration of deep neural network training, the GPU has become the mainstream platform. GPUs face substantial challenges on GNNs, such as workload imbalance and memory acc…
GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption
Kaustubh Shivdikar, Yuhui Bao, Rashmi Agrawal +9
Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure…
Accelerating Polynomial Multiplication for Homomorphic Encryption on GPUs
Kaustubh Shivdikar, Gilbert Jonatan, Evelio Mora +6
Homomorphic Encryption (HE) enables users to securely outsource both the storage and computation of sensitive data to untrusted servers. Not only does HE offer an attractive soluti…