TU Darmstadt / ULB / TUbiblio

Advantages of a Statistical Estimation Approach for Clock Frequency Estimation of Heterogeneous and Irregular CGRAs

Wolf, Dennis Leander ; Spang, Christoph ; Diener, Daniel ; Hochberger, Christian (2022)
Advantages of a Statistical Estimation Approach for Clock Frequency Estimation of Heterogeneous and Irregular CGRAs.
In: ACM Transactions on Reconfigurable Technology and Systems, (Early Access)
doi: 10.1145/3531062
Article

Abstract

Estimating the maximum clock frequency of homogeneous Coarse Grained Reconfigurable Arrays/ Architectures (CGRAs) with an arbitrary number of Processing Elements (PE) is difficult. Clock frequency estimation of highly heterogeneous CGRAs takes additional factors into account, thus is even more difficult. Main challenges are the heterogeneous set of operators for each Processing Element (PE) and the irregular interconnect (connecting a CGRA’s PEs). Multiple estimation approaches could be reasonable. We propose an optimized statistical estimator, which is based on our prior work. We demonstrate its superiority to state of the art neural networks in terms of accuracy and robustness, especially in situations with a sparse set of training data.

Item Type: Article
Erschienen: 2022
Creators: Wolf, Dennis Leander ; Spang, Christoph ; Diener, Daniel ; Hochberger, Christian
Type of entry: Bibliographie
Title: Advantages of a Statistical Estimation Approach for Clock Frequency Estimation of Heterogeneous and Irregular CGRAs
Language: English
Date: 25 April 2022
Place of Publication: New York, NY, USA
Publisher: ACM
Journal or Publication Title: ACM Transactions on Reconfigurable Technology and Systems
Issue Number: Early Access
DOI: 10.1145/3531062
Abstract:

Estimating the maximum clock frequency of homogeneous Coarse Grained Reconfigurable Arrays/ Architectures (CGRAs) with an arbitrary number of Processing Elements (PE) is difficult. Clock frequency estimation of highly heterogeneous CGRAs takes additional factors into account, thus is even more difficult. Main challenges are the heterogeneous set of operators for each Processing Element (PE) and the irregular interconnect (connecting a CGRA’s PEs). Multiple estimation approaches could be reasonable. We propose an optimized statistical estimator, which is based on our prior work. We demonstrate its superiority to state of the art neural networks in terms of accuracy and robustness, especially in situations with a sparse set of training data.

Uncontrolled Keywords: Design Space Exploration, Automation, Machine Learning, Coarse Grained Reconfigurable Architecture, Heterogeneity
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Computer Systems Group
Date Deposited: 26 Oct 2022 07:07
Last Modified: 20 Jun 2023 09:58
PPN: 508920698
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details