MemFlow: Memory-driven data scheduling with datapath co-design in accelerators for large-scale inference applications

Qi Nie, Sharad Malik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

SRAM scratch-pad memory in accelerators is limited in size and bandwidth. Besides computation, accelerator design is about how data flow is scheduled across the memory hierarchy, from DRAM to datapath registers. There is limited support for this in current tools. Thus, we propose MemFlow, memory-driven data scheduling with datapath co-design in accelerators, to improve computing performance and reduce higher-level memory accesses. We demonstrate its efficacy using several key kernels from large-scale inference applications.

Original languageEnglish (US)
Title of host publicationASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages446-451
Number of pages6
ISBN (Electronic)9781509006021
DOIs
StatePublished - Feb 20 2018
Event23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018 - Jeju, Korea, Republic of
Duration: Jan 22 2018Jan 25 2018

Publication series

NameProceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Volume2018-January

Other

Other23rd Asia and South Pacific Design Automation Conference, ASP-DAC 2018
CountryKorea, Republic of
CityJeju
Period1/22/181/25/18

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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  • Cite this

    Nie, Q., & Malik, S. (2018). MemFlow: Memory-driven data scheduling with datapath co-design in accelerators for large-scale inference applications. In ASP-DAC 2018 - 23rd Asia and South Pacific Design Automation Conference, Proceedings (pp. 446-451). (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASPDAC.2018.8297364