36 citations · 70 across the 6 of their papers we have counts for
7 papers
Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi +2
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However,…
FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching
Jianming Tong, Anirudh Itagi, Prasanth Chatarasi +1
The inference of ML models composed of diverse structures, types, and sizes boils down to the execution of different dataflows (i.e. different tiling, ordering, parallelism, and sh…
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
Geonhwa Jeong, Gokcen Kestor, Prasanth Chatarasi +5
To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these "domain-sp…
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…
Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine
Prasanth Chatarasi, Stephen Neuendorffer, Samuel Bayliss +2
Xilinx's AI Engine is a recent industry example of energy-efficient vector processing that includes novel support for 2D SIMD datapaths and shuffle interconnection network. The cur…
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…