MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A systematic approach for architecture-level energy estimation of accelerator designs

Author(s)
Wu, Yannan(Data scientist)Massachusetts Institute of Technology.
Thumbnail
Download1202001387-MIT.pdf (2.392Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Vivienne Sze and Joel S. Emer.
Terms of use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
With Moore's law slowing down and Dennard scaling over, energy-ecient domain-specific accelerators have become a promising way for hardware designers to continue bringing energy eciency improvements to data and computation-intensive applications. To enable 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 thesis presents Accelergy, a generally applicable energy estimation methodology for accelerators that allows flexible specification of designs comprised of user-defined high-level compound components and user-defined low-level primitive components, which can be characterized by third-party energy estimation plugins. We have provided primitive and compound components for modeling deep neural network (DNN) accelerator designs as applications of the proposed methodology. The proposed Accelergy energy estimation framework, which consists of the Accelergy energy estimator and multiple estimation plugins, is validated on Eyeriss, a well-known DNN accelerator design. Overall, with its rich collections of action types and components, Accelergy can achieve 95% accuracy comparing to energy obtained from post-layout simulation in terms of total energy consumption and provide accurate energy breakdowns for components at dierent levels of granularity.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 105-109).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/128303
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.