A method for swift selection of appropriate approximate multipliers for CNN hardware accelerators
A method for swift selection of appropriate approximate multipliers for CNN hardware accelerators
As convolutional neural networks (CNNs) gain traction for embedded device implementation, there's a burgeoning interest in approximate computing technologies for increasing hardware efficiency. Most of the works in this field focus on proposing novel approximate hardware units and structures, but structured guidance for selecting optimal approximate calculation techniques for CNN accelerators remains scant. This paper introduces a novel error injection technique, leveraging the error rate matrix of approximate multipliers (AxMs), called Error Matrix Based Error Injected (EMEI). This facilitates the swift selection of appropriate AxMs for each PE in the CNN hardware accelerator. In addition, this approach is applied to a MobileNetV2-based CNN model on the CIFAR-10 dataset to demonstrate the performance. Experimental results show that our method adeptly optimises hardware resources by combining AxMs with different accuracy levels while ensuring accuracy. This innovation paves the way for streamlined CNN accelerator designs in embedded systems.
Approximate computing, Approximate multiplier, CNN, CNN hardware accelerator
Sun, Peiyao
e517faec-75c2-43e4-a45e-90f47e80d195
Yu, Haosen
71ded974-69df-4edd-8c1a-ad192b83a27e
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Kazmierski, Tomasz
a97d7958-40c3-413f-924d-84545216092a
20 May 2024
Sun, Peiyao
e517faec-75c2-43e4-a45e-90f47e80d195
Yu, Haosen
71ded974-69df-4edd-8c1a-ad192b83a27e
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Kazmierski, Tomasz
a97d7958-40c3-413f-924d-84545216092a
Sun, Peiyao, Yu, Haosen, Halak, Basel and Kazmierski, Tomasz
(2024)
A method for swift selection of appropriate approximate multipliers for CNN hardware accelerators.
2024 International Symposium on Circuits and Systems, Resorts World Convention Centre, Singapore.
19 - 22 May 2024.
5 pp
.
Record type:
Conference or Workshop Item
(Poster)
Abstract
As convolutional neural networks (CNNs) gain traction for embedded device implementation, there's a burgeoning interest in approximate computing technologies for increasing hardware efficiency. Most of the works in this field focus on proposing novel approximate hardware units and structures, but structured guidance for selecting optimal approximate calculation techniques for CNN accelerators remains scant. This paper introduces a novel error injection technique, leveraging the error rate matrix of approximate multipliers (AxMs), called Error Matrix Based Error Injected (EMEI). This facilitates the swift selection of appropriate AxMs for each PE in the CNN hardware accelerator. In addition, this approach is applied to a MobileNetV2-based CNN model on the CIFAR-10 dataset to demonstrate the performance. Experimental results show that our method adeptly optimises hardware resources by combining AxMs with different accuracy levels while ensuring accuracy. This innovation paves the way for streamlined CNN accelerator designs in embedded systems.
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Accepted/In Press date: 2024
Published date: 20 May 2024
Venue - Dates:
2024 International Symposium on Circuits and Systems, Resorts World Convention Centre, Singapore, 2024-05-19 - 2024-05-22
Keywords:
Approximate computing, Approximate multiplier, CNN, CNN hardware accelerator
Identifiers
Local EPrints ID: 488054
URI: http://eprints.soton.ac.uk/id/eprint/488054
PURE UUID: 37ac455a-fafe-4265-86da-4879640f41cf
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Date deposited: 14 Mar 2024 17:31
Last modified: 21 May 2024 02:05
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