A Column Streaming-Based Convolution Engine and Mapping Algorithm for CNN-based Edge AI Accelerators

Weison Lin, Tughrul Arslan

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

Abstract / Description of output

Edge AI accelerators have been emerging as a solution for near customers' applications in areas such as image recognition sensors, remote sensing satellites, robotics, wearable devices, and drones. These applications require meeting performance targets and strict area and power constraints due to their portable mobility feature and limited power sources. As a result, a column streaming-based convolution engine has been proposed in this paper that includes column sets of processing elements design for flexibility in terms of the applicability for different CNN algorithms in edge AI accelerators. Compared to a commercialized CNN accelerator, the key results reveal that the column streaming-based convolution engine requires similar execution cycles to process a 227 × 227 feature map and avoid zero-padding penalties.

Original languageEnglish
Title of host publication2021 28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781728182810
DOIs
Publication statusPublished - 10 Jan 2022
Event28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021 - Dubai, United Arab Emirates
Duration: 28 Nov 20211 Dec 2021

Conference

Conference28th IEEE International Conference on Electronics, Circuits, and Systems, ICECS 2021
Country/TerritoryUnited Arab Emirates
CityDubai
Period28/11/211/12/21

Keywords / Materials (for Non-textual outputs)

  • CNN mapping algorithm
  • convolution engine
  • edge AI accelerators
  • PERFORMANCE EVALUATION
  • satellites
  • Convolution
  • Wearable computers
  • AI accelerators
  • Streaming media
  • Robot sensing systems

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