PYXIS: An Open-Source Performance Dataset Of Sparse Accelerators
Linghao Song, Yuze Chi, Jason Cong
Abstract
Customized accelerators provide gains of performance and efficiency in specific domains of applications. Sparse data structures and/or representations exist in a wide range of applications. However, it is challenging to design accelerators for sparse applications because no architecture or performance-level analytic models are able to fully capture the spectrum of the sparse data. Accelerator researchers rely on real execution to get precise feedback for their designs. In this work, we present PYXIS, a performance dataset for customized accelerators on sparse data. PYXIS collects accelerator designs and real execution performance statistics. Currently, there are 73.8 K instances in PYXIS. PYXIS is open-source, and we are constantly growing PYXIS with new accelerator designs and performance statistics. PYXIS can be a benefit to researchers in the fields of accelerator, architecture, performance, algorithm and many related topics.
BibTeX
@inproceedings{icassp2022_pyxisanopensourc,
title = {PYXIS: An Open-Source Performance Dataset Of Sparse Accelerators},
author = {Linghao Song and Yuze Chi and Jason Cong},
booktitle = {ICASSP 2022},
year = {2022}
}