M2DA: A Low-Complex Design Methodology for Convolutional Neural Network Exploiting Data Symmetry and Redundancy

Panwar, Madhuri and Sri Hari, Nemani and Biswas, Dwaipayan and Acharyya, Amit (2021) M2DA: A Low-Complex Design Methodology for Convolutional Neural Network Exploiting Data Symmetry and Redundancy. Circuits, Systems, and Signal Processing, 40 (3). pp. 1542-1567. ISSN 0278-081X

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Convolutional neural network (CNN) is one of the most dominant deep learning networks with good generalization ability. Its high performance in solving large and complex learning problems has enabled usability in IoT devices. However, CNN involves a substantial amount of convolution operations, which demand a large number of power-consuming multipliers. This hinders the deployment of deep CNNs on mobile and IoT edge devices owing to restricted power–area constraints. In this paper, we propose a low-complex methodology named ‘minimal modified distributed arithmetic’ (M2DA) for convolutional neural network (CNN) by exploiting the data symmetry and consequently storing only the unique kernel coefficient’s combinations and the size of required memory and multiplication operations can be reduced, leading to power–area efficient design. For validation, a low-complex CNN architecture for activity recognition application is designed and synthesized in Synopsys using the UMC 65 nm technology wherein average 36.89% and 51.63% improvement is achieved in power and area, respectively, compared to conventional MDA methodology. To demonstrate the significance of the proposed M2DA methodology, we have also implemented the Alexnet which is the most widely and publicly available CNN model for the image classification problem.

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IITH Creators:
IITH CreatorsORCiD
Panwar, MadhuriUNSPECIFIED
Acharyya, Amithttp://orcid.org/0000-0002-5636-0676
Item Type: Article
Uncontrolled Keywords: Activity recognition; Complex designs; Complex learning; Distributed arithmetic; Generalization ability; Learning network; Multiplication operations; Power consuming;Complex networks; Convolution; Deep learning; Internet of things; Software architecture
Subjects: Electrical Engineering
Electrical Engineering > Electrical and Electronic
Electrical Engineering > Instruments and Instrumentation
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 22 Jun 2021 04:00
Last Modified: 22 Jun 2021 04:00
URI: http://raiith.iith.ac.in/id/eprint/7968
Publisher URL: http://doi.org/10.1007/s00034-020-01534-3
OA policy: https://v2.sherpa.ac.uk/id/publication/15622
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