3 citations · 4 across the 11 of their papers we have counts for
4 papers · 1 filter
DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4
High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large La…
PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures
Christina Giannoula, Peiming Yang, Ivan Fernandez +7
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kerne…
ACS: Concurrent Kernel Execution on Irregular, Input-Dependent Computational Graphs
Sankeerth Durvasula, Adrian Zhao, Raymond Kiguru +3
GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines…
VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks
Sankeerth Durvasula, Raymond Kiguru, Samarth Mathur +3
Real-time 3D mapping is a critical component in many important applications today including robotics, AR/VR, and 3D visualization. 3D mapping involves continuously fusing depth map…