Pyxis: An Open-Source Performance Dataset of Sparse Accelerators
Not on the map. Why?
Abstract (arXiv)
Specialized accelerators provide gains of performance and efficiency in specific domains of applications. Sparse data structures or/and 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 specialized 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 benefit researchers in the fields of accelerator, architecture, performance, algorithm, and many related topics.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 34 references that are in the map. First posted on arXiv 8 Oct 2021.
Builds on
8 of 34 references.
References · 8
- 2017Attention Is All You Needcited by 59,932
- 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
- 2019MLPerf Inference Benchmarkcited by 195
- 2018SimGNN: A Neural Network Approach to Fast Graph Similarity Computationcited by 89
- 2021IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learningcited by 26
- 2020AutoDSE: Enabling Software Programmers to Design Efficient FPGA Acceleratorscited by 25
- 2017GraphR: Accelerating Graph Processing Using ReRAMcited by 19
- 2021Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplicationcited by 6
Built on by
Not cited yet.