MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Mixed-precision architecture for flexible neural network accelerators

Author(s)
Hafdi, Driss.
Thumbnail
Download1145118397-MIT.pdf (1.275Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Song Han.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
Model quantization provides considerable latency and energy consumption reductions while preserving accuracy. However, the optimal bitwidth reduction varies on a layer by layer basis. This thesis suggests a novel neural network accelerator architecture that handles multiple bit precisions for both weights and activations. The architecture is based on a fused spatial and temporal micro-architecture that maximizes both bandwidth eciency and computational ability. Furthermore, this thesis presents an FPGA implementation of this new mixed precision architecture and it discusses the ISA and its associated bitcode compiler. Finally, the performance of the system is evaluated on a Virtex-9 UltraScale FPGA by running state-of-the-art neural networks.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 89-91).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/124247
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Electrical Engineering and Computer Sciences - Master's degree
  • Electrical Engineering and Computer Sciences - Master's degree

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Facebook Instagram YouTube RSS

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.