Prasanth Chatarasi

Orcid: 0000-0002-0974-4001

According to our database1, Prasanth Chatarasi authored at least 16 papers between 2015 and 2024.

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Bibliography

2024

FEATHER: A Reconfigurable Accelerator with Data Reordering Support for Low-Cost On-Chip Dataflow Switching.
Proceedings of the 51st ACM/IEEE Annual International Symposium on Computer Architecture, 2024

2022
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication.
IEEE Trans. Parallel Distributed Syst., 2022

Marvel: A Data-Centric Approach for Mapping Deep Learning Operators on Spatial Accelerators.
ACM Trans. Archit. Code Optim., 2022

2021
Advancing Compiler Optimizations for General-Purpose & Domain-Specific Parallel Architectures.
PhD thesis, 2021

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators.
Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques, 2021

2020
MAESTRO: A Data-Centric Approach to Understand Reuse, Performance, and Hardware Cost of DNN Mappings.
IEEE Micro, 2020

MARVEL: A Decoupled Model-driven Approach for Efficiently Mapping Convolutions on Spatial DNN Accelerators.
CoRR, 2020

Vyasa: A High-Performance Vectorizing Compiler for Tensor Convolutions on the Xilinx AI Engine.
Proceedings of the 2020 IEEE High Performance Extreme Computing Conference, 2020

2019
Understanding Reuse, Performance, and Hardware Cost of DNN Dataflow: A Data-Centric Approach.
Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019

Experimental Insights from the Rogues Gallery.
Proceedings of the 2019 IEEE International Conference on Rebooting Computing, 2019

2018
A Preliminary Study of Compiler Transformations for Graph Applications on the Emu System.
Proceedings of the Workshop on Memory Centric High Performance Computing, 2018

A Unified Approach to Variable Renaming for Enhanced Vectorization.
Proceedings of the Languages and Compilers for Parallel Computing, 2018

2016
An Extended Polyhedral Model for SPMD Programs and Its Use in Static Data Race Detection.
Proceedings of the Languages and Compilers for Parallel Computing, 2016

2015
Polyhedral Optimizations of Explicitly Parallel Programs.
Proceedings of the 2015 International Conference on Parallel Architectures and Compilation, 2015

Extending Polyhedral Model for Analysis and Transformation of OpenMP Programs.
Proceedings of the 2015 International Conference on Parallel Architectures and Compilation, 2015


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