3 citations · 3 across the 8 of their papers we have counts for
9 papers
Defeat the Heap: Zero-Copy Data Movement in AXI4MLIR
Elam Cohavi, Nicolas Bohm Agostini, Jude Haris +3
As custom hardware accelerators become increasingly central to machine learning workloads, efficient data transfer is critical for maximizing accelerator performance on linear alge…
PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
Rappy Saha, Jude Haris, Nicolas Bohm Agostini +2
Power-of-two (PoT) quantization significantly reduces the size of deep neural networks (DNNs) and replaces multiplications with bit-shift operations for inference. Prior work has s…
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…
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…
Memory Efficient Multithreaded Incremental Segmented Sieve Algorithm
Evan Ning, David Kaeli
Prime numbers are fundamental in number theory and play a significant role in various areas, from pure mathematics to practical applications, including cryptography. In this contri…