14 citations · 19 across the 5 of their papers we have counts for
5 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…
Flexagon: A Multi-Dataflow Sparse-Sparse Matrix Multiplication Accelerator for Efficient DNN Processing
Francisco Muñoz-Martínez, Raveesh Garg, José L. Abellán +3
Sparsity is a growing trend in modern DNN models. Existing Sparse-Sparse Matrix Multiplication (SpMSpM) accelerators are tailored to a particular SpMSpM dataflow (i.e., Inner Produ…
Understanding the Design-Space of Sparse/Dense Multiphase GNN dataflows on Spatial Accelerators
Raveesh Garg, Eric Qin, Francisco Muñoz-Martínez +8
Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applicat…
MGPU-TSM: A Multi-GPU System with Truly Shared Memory
Saiful A. Mojumder, Yifan Sun, Leila Delshadtehrani +6
The sizes of GPU applications are rapidly growing. They are exhausting the compute and memory resources of a single GPU, and are demanding the move to multiple GPUs. However, the p…
STONNE: A Detailed Architectural Simulator for Flexible Neural Network Accelerators
Francisco Muñoz-Martínez, José L. Abellán, Manuel E. Acacio +1
The design of specialized architectures for accelerating the inference procedure of Deep Neural Networks (DNNs) is a booming area of research nowadays. First-generation rigid propo…