A Theoretical CNN Compression Framework for Resource-Restricted Environments

Awan, Zahra Waheed; Khalid, Shehzad; Gul, Saba

doi:10.1109/icodt255437.2022.9787431

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…A remoc ¸ão dos pesos do modelo pode ser feita de forma consciente, onde os pesos são removidos sistematicamente durante o processo de aprendizagem, chamado de "aware pruning" [Awan et al 2022], ou também pode ser feita após o treinamento, chamado de "post-training pruning"(PTP), onde os pesos são removidos somente após o modelo ter sido treinado. Trabalhos na literatura mostram que o processo de poda consciente possui resultados melhores do que o PTP, dado que a poda consciente forc ¸a o algoritmo de treinamento a encontrar uma nova soluc ¸ão para o valor dos pesos, diferente do PTP [He et al 2022].…”

Section: Compressão De Modelo Por Podaunclassified

Estratégia de Treinamento para Poda Consciente de Modelo de CNN Aplicado a Classificação de Imagens

Goldbarg,

Silva,

da Silva

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Este artigo apresenta uma nova estratégia de treinamento para compressão de modelos de redes neurais convolucionais (Convolutional Neural Networks - CNN). A estratégia proposta utiliza um esquema de poda consciente dos pesos da CNN, diferenciando-se das abordagens convencionais. Neste trabalho, a poda consciente é aplicada de forma contínua durante todo o processo de treinamento, em todos os mini-batches. A estratégia foi aplicada em um problema de classificação de 10 mil imagens, pertencentes a 10 classes diferentes, utilizando o dataset CIFAR-10. Os resultados obtidos demonstraram que foi possível remover aproximadamente 82% dos parâmetros da CNN, mantendo uma alta acurácia. Esses resultados evidenciam a eficácia da técnica de remoção de pesos por poda consciente para essa aplicação específica.

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Section: Compressão De Modelo Por Podaunclassified

Estratégia de Treinamento para Poda Consciente de Modelo de CNN Aplicado a Classificação de Imagens

Goldbarg,

Silva,

da Silva

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing

Marković,

Stamenković,

Đorđević

et al. 2024

Sensors

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The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).

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A Theoretical CNN Compression Framework for Resource-Restricted Environments

Cited by 2 publications

References 32 publications

Estratégia de Treinamento para Poda Consciente de Modelo de CNN Aplicado a Classificação de Imagens

Estratégia de Treinamento para Poda Consciente de Modelo de CNN Aplicado a Classificação de Imagens

Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing

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