16 citations · 69 across the 23 of their papers we have counts for
6 papers · 1 filter
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication
Gordon E. Moon, Hyoukjun Kwon, Geonhwa Jeong +3
There is a growing interest in custom spatial accelerators for machine learning applications. These accelerators employ a spatial array of processing elements (PEs) interacting via…
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats
Eric Qin, Geonhwa Jeong, William Won +7
Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential mem…
Marvel: A Data-centric Compiler for DNN Operators on Spatial Accelerators
Prasanth Chatarasi, Hyoukjun Kwon, Natesh Raina +6
The efficiency of a spatial DNN accelerator depends heavily on the compiler and its cost model ability to generate optimized mappings for various operators of DNN models on to the…
Heterogeneous Dataflow Accelerators for Multi-DNN Workloads
Hyoukjun Kwon, Liangzhen Lai, Michael Pellauer +3
Emerging AI-enabled applications such as augmented/virtual reality (AR/VR) leverage multiple deep neural network (DNN) models for sub-tasks such as object detection, hand tracking,…
SCALE-Sim: Systolic CNN Accelerator Simulator
Ananda Samajdar, Yuhao Zhu, Paul Whatmough +2
Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplic…
Understanding Reuse, Performance, and Hardware Cost of DNN Dataflows: A Data-Centric Approach Using MAESTRO
Hyoukjun Kwon, Prasanth Chatarasi, Michael Pellauer +3
The data partitioning and scheduling strategies used by DNN accelerators to leverage reuse and perform staging are known as dataflow, and they directly impact the performance and e…