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Bare‐Bones particle Swarm optimization‐based quantization for fast and energy efficient convolutional neural networks

Tmamna, Jihene; Ayed, Emna Ben; Fourati, Rahma; Hussain, Amir; Ayed, Mounir Ben


Jihene Tmamna

Emna Ben Ayed

Rahma Fourati

Mounir Ben Ayed


Neural network quantization is a critical method for reducing memory usage and computational complexity in deep learning models, making them more suitable for deployment on resource-constrained devices. In this article, we propose a method called BBPSO-Quantizer, which utilizes an enhanced Bare-Bones Particle Swarm Optimization algorithm, to address the challenging problem of mixed precision quantization of convolutional neural networks (CNNs). Our proposed algorithm leverages a new population initialization, a robust screening process, and a local search strategy to improve the search performance and guide the population towards a feasible region. Additionally, Deb's constraint handling method is incorporated to ensure that the optimized solutions satisfy the functional constraints. The effectiveness of our BBPSO-Quantizer is evaluated on various state-of-the-art CNN architectures, including VGG, DenseNet, ResNet, and MobileNetV2, using CIFAR-10, CIFAR-100, and Tiny ImageNet datasets. Comparative results demonstrate that our method delivers an excellent tradeoff between accuracy and computational efficiency.

Journal Article Type Article
Acceptance Date Nov 27, 2023
Online Publication Date Dec 17, 2023
Publication Date 2024-04
Deposit Date Jan 12, 2024
Publicly Available Date Dec 18, 2024
Journal Expert Systems
Print ISSN 0266-4720
Electronic ISSN 1468-0394
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 41
Issue 4
Article Number e13522
Keywords Barebone PSO, energy efficient model inference, mixed precision quantization, model compression
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