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Doubly-Block Circulant Kernel Matrix Exploitation in Convolutional Accelerators

Ferreira, Lucas LU ; Malkowsky, Steffen LU ; Persson, Patrik LU orcid ; Astrom, Karl LU orcid and Liu, Liang LU orcid (2023) 2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023 p.236-240
Abstract

In this paper, we present a novel algorithmic and hardware co-design approach specifically tailored for efficient 2D convolution implementations, a crucial operation in convolutional neural networks (CNNs). Our method addresses the limitations of existing software-based solutions and hardware-based architectures, delivering significant improvements in asymptotic behavior for generic convolution cases. By leveraging the distinctive geometry of doubly block circulant unrolled kernel matrices, our approach eliminates the need for input and weight buffers, optimizes output memory usage, and minimizes redundant memory accesses. A comprehensive comparative analysis with state-of-the-art techniques showcases the key advantages and superior... (More)

In this paper, we present a novel algorithmic and hardware co-design approach specifically tailored for efficient 2D convolution implementations, a crucial operation in convolutional neural networks (CNNs). Our method addresses the limitations of existing software-based solutions and hardware-based architectures, delivering significant improvements in asymptotic behavior for generic convolution cases. By leveraging the distinctive geometry of doubly block circulant unrolled kernel matrices, our approach eliminates the need for input and weight buffers, optimizes output memory usage, and minimizes redundant memory accesses. A comprehensive comparative analysis with state-of-the-art techniques showcases the key advantages and superior performance of our proposed method, achieving substantial reductions in memory requirements and high throughput.

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author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
2D Convolution, Doubly-Blocked Circulant Matrix, Systolic Array, Unrolled Kernel Matrix
host publication
2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
conference location
Tempe, United States
conference dates
2023-08-06 - 2023-08-09
external identifiers
  • scopus:85185371236
ISBN
9798350302103
DOI
10.1109/MWSCAS57524.2023.10406059
language
English
LU publication?
yes
id
75f5ede4-3210-4e97-8eeb-b97f5ffdc838
date added to LUP
2024-03-18 16:09:25
date last changed
2025-04-04 14:01:11
@inproceedings{75f5ede4-3210-4e97-8eeb-b97f5ffdc838,
  abstract     = {{<p>In this paper, we present a novel algorithmic and hardware co-design approach specifically tailored for efficient 2D convolution implementations, a crucial operation in convolutional neural networks (CNNs). Our method addresses the limitations of existing software-based solutions and hardware-based architectures, delivering significant improvements in asymptotic behavior for generic convolution cases. By leveraging the distinctive geometry of doubly block circulant unrolled kernel matrices, our approach eliminates the need for input and weight buffers, optimizes output memory usage, and minimizes redundant memory accesses. A comprehensive comparative analysis with state-of-the-art techniques showcases the key advantages and superior performance of our proposed method, achieving substantial reductions in memory requirements and high throughput.</p>}},
  author       = {{Ferreira, Lucas and Malkowsky, Steffen and Persson, Patrik and Astrom, Karl and Liu, Liang}},
  booktitle    = {{2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023}},
  isbn         = {{9798350302103}},
  keywords     = {{2D Convolution; Doubly-Blocked Circulant Matrix; Systolic Array; Unrolled Kernel Matrix}},
  language     = {{eng}},
  pages        = {{236--240}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Doubly-Block Circulant Kernel Matrix Exploitation in Convolutional Accelerators}},
  url          = {{http://dx.doi.org/10.1109/MWSCAS57524.2023.10406059}},
  doi          = {{10.1109/MWSCAS57524.2023.10406059}},
  year         = {{2023}},
}