skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Attention:

The NSF Public Access Repository (PAR) system and access will be unavailable from 11:00 PM ET on Friday, May 16 until 2:00 AM ET on Saturday, May 17 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Yang, Zhenkun"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; (, Lecture notes in computer science)
    Li, Yi; Tahar, Sofiene (Ed.)
    Machine learning accelerators (MLAs) are increasingly im- portant in many applications such as image and video processing, speech recognition, and natural language processing. To achieve the needed per- formances and power efficiencies, MLAs are highly concurrent. The cor- rectness of MLAs hinges on the concept of sequential consistency, i.e., the concurrent execution of a program by an MLA must be equivalent to a sequential execution of the program. In this paper, we certify the sequential consistency of modular MLAs using theorem proving. We őrst provide a formalization of the MLAs and deőne their sequential consis- tency. After that, we introduce our certiőcation methodology based on inductive theorem proving. Finally, we demonstrate the feasibility of our approach through the analysis of the NVIDIA Deep Learning Accelerator and the Versatile Tensor Accelerator 
    more » « less
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; ; et al (, ACM/IEEE Design Automation Conference (DAC))
    Full Text Available 
  3. ; ; ; ; (, 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER))
    Full Text Available