Ghouthi Boukli hacene

Ciaran Bannon

Alberto Delmas Lascorz

Matthieu Courbariaux

Omar Mohamed Awad

Isak Edo Vivancos

Andreas Moshovos

Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer quantization, yielding both execution time … (voir plus)and energy benefits on existing hardware designs that support short bitlengths. However, the question of finding the minimum bitlength for a desired accuracy remains open. We introduce a training method for minimizing inference bitlength at any granularity while maintaining accuracy. Namely, we propose a regularizer that penalizes large bitlength representations throughout the architecture and show how it can be modified to minimize other quantifiable criteria, such as number of operations or memory footprint. We demonstrate that our method learns thrifty representations while maintaining accuracy. With ImageNet, the method produces an average per layer bitlength of 4.13, 3.76 and 4.36 bits on AlexNet, ResNet18 and MobileNet V2 respectively, remaining within 2.0%, 0.5% and 0.5% of the base TOP-1 accuracy.

2024-05-19

2024 IEEE International Symposium on Circuits and Systems (ISCAS) (publié)

SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning

Luca Zampierin

Bac Nguyen

Mirco Ravanelli

2024-04-14

2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (publié)

Attention Based Pruning for Shift Networks

Carlos Lassance

Matthieu Courbariaux

In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, i… (voir plus)t is often required to assemble a large number of CLs, each containing thousands of parameters, in order to reach state-of-the-art accuracy, thus resulting in complex and demanding systems that are poorly fitted to resource-limited devices. Recently, methods have been proposed to replace the generic convolution operator by the combination of a shift operation and a simpler

2021-01-10

2020 25th International Conference on Pattern Recognition (ICPR) (publié)

Quantized Guided Pruning for Efficient Hardware Implementations of Deep Neural Networks

Matthieu Arzel

Nicolas Farrugia

Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in particular are state-of-the-art in numerous computer visi… (voir plus)on tasks such as object classification and detection. However, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, by replacing the complex convolutional operation by a low-cost multiplexer. We perform experiments on CIFAR10, CIFAR100 and SVHN datasets and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints compared to the baselines. We also propose an efficient hardware architecture, implemented on Field Programmable Gate Arrays (FPGAs), to accelerate inference, which works as a pipeline and accommodates multiple layers working at the same time to speed up the inference process. In contrast with most proposed approaches which have used external memory or software defined memory controllers, our work is based on algorithmic optimization and full-hardware design, enabling a direct, on-chip memory implementation of a DNN while keeping close to state of the art accuracy.

2020-06-16

2020 18th IEEE International New Circuits and Systems Conference (NEWCAS) (publié)

Pruning for efficient hardware implementations of deep neural networks

Matthieu Arzel

Nicolas Farrugia

Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks

Matthieu Arzel

Nicolas Farrugia

Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. How… (voir plus)ever, the large amount of parameters they contain leads to a high computational complexity and strongly limits their usability in budget-constrained devices such as embedded devices. In this paper, we propose a combination of a new pruning technique and a quantization scheme that effectively reduce the complexity and memory usage of convolutional layers of CNNs, and replace the complex convolutional operation by a low-cost multiplexer. We perform experiments on the CIFAR10, CIFAR100 and SVHN and show that the proposed method achieves almost state-of-the-art accuracy, while drastically reducing the computational and memory footprints. We also propose an efficient hardware architecture to accelerate CNN operations. The proposed hardware architecture is a pipeline and accommodates multiple layers working at the same time to speed up the inference process.

2018-12-25

ArXiv (prépublication)