Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs
Name
2019_iccad_accelergy_ilp.pdf
Size
1.57 MB
Format
Adobe PDF
Checksum (MD5)
77b061aba73d485e5c09201389533ba5
Author(s)
Wu, Yannan Nellie
Emer, Joel S
Sze, Vivienne
Date Issued
November 2019
Journal
International Conference On Computer Aided Design (ICCAD)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wu, Yannan Nellie et al. "Accelergy: An Architecture-Level Energy Estimation Methodology for Accelerator Designs." International Conference On Computer Aided Design (ICCAD), November 2019, Westminster, Colorado, United States of America, Institute of Electrical and Electronics Engineers (IEEE), November 2019
Version
Author's final manuscript
Abstract
With Moore’s law slowing down and Dennard scaling ended, energy efficient domain-specific accelerators, such as deep neural network (DNN) processors for machine learning and programmable network switches for cloud applications, have become a promising way for hardware designers to continue bringing energy efficiency improvements to data and computation-intensive applications. To ensure the fast exploration of the accelerator design space, architecture level energy estimators, which perform energy estimations without requiring complete hardware description of the designs, are critical to designers. However, it is difficult to use existing architecture level energy estimators to obtain accurate estimates for accelerator designs, as accelerator designs are diverse and sensitive to data patterns. This paper presents Accelergy, a generally applicable energy estimation methodology for accelerators that allows design specifications comprised of user-defined high-level compound components and user-defined low-level primitive components, which can be characterized by third-party energy estimation plug-ins. An example with primitive and compound components for DNN accelerator designs is also provided as an application of the proposed methodology. Overall, Accelergy achieves 95% accuracy on Eyeriss, a well-known DNN accelerator design, and can correctly capture the energy breakdown of components at different granularities. The Accelergy code is available at http://accelergy.mit.edu.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Microsystems Technology Laboratories
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://iccad.com/2019_accepted_papers
