Document Type

Conference Proceeding

Publication Date

3-25-2024

Publication Title

29th Asia and South Pacific Design Automation Conference (ASP-DAC 2024)

Pages

1-6

Publisher Name

IEEE

Abstract

Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energy-efficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy.

Comments

Author Posting © 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the author's version of the work. This is a pre-print of a paper presented at the 29th Asia and South Pacific Design Automation Conference (ASP-DAC 2024). The final published version of the work is published at- https://doi.org/10.1109/ASP-DAC58780.2024.10473884

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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