3 citations · 5 across the 6 of their papers we have counts for
6 papers
HARP: A Taxonomy for Heterogeneous and Hierarchical Processors for Mixed-reuse Workloads
Raveesh Garg, Michael Pellauer, Tushar Krishna
Artificial intelligence (AI) application domains consist of a mix of tensor operations with high and low arithmetic intensities (aka reuse). Hierarchical (i.e. compute along multip…
PipeOrgan: Efficient Inter-operation Pipelining with Flexible Spatial Organization and Interconnects
Raveesh Garg, Hyoukjun Kwon, Eric Qin +3
Because of the recent trends in Deep Neural Networks (DNN) models being memory-bound, inter-operator pipelining for DNN accelerators is emerging as a promising optimization. Inter-…
CELLO: Co-designing Schedule and Hybrid Implicit/Explicit Buffer for Complex Tensor Reuse
Raveesh Garg, Michael Pellauer, Sivasankaran Rajamanickam +1
Tensor algebra accelerators have been gaining popularity for running high-performance computing (HPC) workloads. Identifying optimal schedules for individual tensor operations and…
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…
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya +5
Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common ch…
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…