NAS-BNN: Neural Architecture Search for Binary Neural Networks

Fuente: arXiv

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Bibliographic Details
Main Authors:	Lin, Zhihao, Wang, Yongtao, Zhang, Jinhe, Chu, Xiaojie, Ling, Haibin
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
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author	Lin, Zhihao Wang, Yongtao Zhang, Jinhe Chu, Xiaojie Ling, Haibin
author_facet	Lin, Zhihao Wang, Yongtao Zhang, Jinhe Chu, Xiaojie Ling, Haibin
contents	Binary Neural Networks (BNNs) have gained extensive attention for their superior inferencing efficiency and compression ratio compared to traditional full-precision networks. However, due to the unique characteristics of BNNs, designing a powerful binary architecture is challenging and often requires significant manpower. A promising solution is to utilize Neural Architecture Search (NAS) to assist in designing BNNs, but current NAS methods for BNNs are relatively straightforward and leave a performance gap between the searched models and manually designed ones. To address this gap, we propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. We first carefully design a search space based on the unique characteristics of BNNs. Then, we present three training strategies, which significantly enhance the training of supernet and boost the performance of all subnets. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. For instance, we achieve 68.20% top-1 accuracy on ImageNet with only 57M OPs. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS COCO dataset. The source code and models will be released at https://github.com/VDIGPKU/NAS-BNN.
format	Preprint
id	arxiv_https___arxiv_org_abs_2408_15484
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	NAS-BNN: Neural Architecture Search for Binary Neural Networks Lin, Zhihao Wang, Yongtao Zhang, Jinhe Chu, Xiaojie Ling, Haibin Computer Vision and Pattern Recognition Binary Neural Networks (BNNs) have gained extensive attention for their superior inferencing efficiency and compression ratio compared to traditional full-precision networks. However, due to the unique characteristics of BNNs, designing a powerful binary architecture is challenging and often requires significant manpower. A promising solution is to utilize Neural Architecture Search (NAS) to assist in designing BNNs, but current NAS methods for BNNs are relatively straightforward and leave a performance gap between the searched models and manually designed ones. To address this gap, we propose a novel neural architecture search scheme for binary neural networks, named NAS-BNN. We first carefully design a search space based on the unique characteristics of BNNs. Then, we present three training strategies, which significantly enhance the training of supernet and boost the performance of all subnets. Our discovered binary model family outperforms previous BNNs for a wide range of operations (OPs) from 20M to 200M. For instance, we achieve 68.20% top-1 accuracy on ImageNet with only 57M OPs. In addition, we validate the transferability of these searched BNNs on the object detection task, and our binary detectors with the searched BNNs achieve a novel state-of-the-art result, e.g., 31.6% mAP with 370M OPs, on MS COCO dataset. The source code and models will be released at https://github.com/VDIGPKU/NAS-BNN.
title	NAS-BNN: Neural Architecture Search for Binary Neural Networks
topic	Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2408.15484