Morph: Flexible acceleration for 3D CNN-based video understanding

Kartik Hegde, Rohit Agrawal, Yulun Yao, Christopher W. Fletcher

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

Abstract

The past several years have seen both an explosion in the use of Convolutional Neural Networks (CNNs) and accelerators to make CNN inference practical. In the architecture community, the lion share of effort has targeted CNN inference for image recognition. The closely related problem of video recognition has received far less attention as an accelerator target. This is surprising, as video recognition is more computationally intensive than image recognition, and video traffic is predicted to be the majority of internet traffic in the coming years. This paper fills the gap between algorithmic and hardware advances for video recognition by providing a design space exploration and flexible architecture for accelerating 3D Convolutional Neural Networks (3D CNNs)-the core kernel in modern video understanding. When compared to (2D) CNNs used for image recognition, efficiently accelerating 3D CNNs poses a significant engineering challenge due to their large (and variable over time) memory footprint and higher dimensionality. To address these challenges, we design a novel accelerator called 'Morph,' that can adaptively support different spatial and temporal tiling strategies depending on the needs of each layer of each target 3D CNN. We codesign a software infrastructure alongside the Morph hardware to find good-fit parameters to control the hardware. Evaluated on state-of-The-Art 3D CNNs, Morph achieves up to 2.7× (1.9× average) reduction in energy consumption and improves performance/watt up to 4.4× (3× average) compared to a baseline 3D CNN accelerator, with an area overhead of 2%. Morph further achieves a 11.6× average energy reduction on 3D CNNs when compared to Eyeriss, a popular 2D CNN accelerator, while reducing efficiency compared to Eyeriss on a 2D CNN by 71%.

Original languageEnglish (US)
Title of host publicationProceedings - 51st Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2018
PublisherIEEE Computer Society
Pages933-946
Number of pages14
ISBN (Electronic)9781538662403
DOIs
StatePublished - Dec 12 2018
Event51st Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2018 - Fukuoka, Japan
Duration: Oct 20 2018Oct 24 2018

Publication series

NameProceedings of the Annual International Symposium on Microarchitecture, MICRO
Volume2018-October
ISSN (Print)1072-4451

Other

Other51st Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2018
Country/TerritoryJapan
CityFukuoka
Period10/20/1810/24/18

Keywords

  • 3D Convolutional Neural Networks
  • Dataflow
  • Hardware acceleration
  • Hardware/Software codesign
  • Video recognition

ASJC Scopus subject areas

  • Hardware and Architecture

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