Dear Colleagues,

In recent years, deep neural networks (DNNs) have achieved overwhelming success in different artificial intelligence applications. This great success is mainly due to the availability of the GPU and TPU clusters that can train very deep models with thousands of layers and millions/billions of parameters on large-scale datasets. However, such cumbersome DNNs require heavy computation resources, which makes their deployment on devices with limited computational capacity and memory (embedded devices, mobile phones, etc.) very difficult. To overcome this limitation, algorithmic, architectural, and technological efforts could be made. From the algorithmic point of view, DNN compression techniques seem to be an attractive solution. Moreover, some innovative architectures and design flows have been attempted for deployment to reach the compromise between precision and energy efficiency. To summarize, the main challenge is to propose heavy architectures or optimized algorithms that achieve approximately the same performance when compared to the original versions.

This Special Issue aims to cover the new developments and recent advances in the compression of the deep neural networks for real-time applications. The topic includes but is not limited to the following:

• Cloud/FoG/Edge DNN challenges;
• Knowledge distillation;
• Parameters pruning and quantization;
• Design flow and low power systems;
• Low-rank factorization;
• Transferred compact convolutional filters;
• Hardware accelerators.

Dr. Jridi Maher
Dr. Thibault Napoléon
Dr. Ayoub Karine
Guest Editors

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• deep neural networks
• knowledge distillation
• pruning/quantization model compression
• computer visions
• internet of things
• CNN
• FPGA, SoPC, GPU
• IoMT : internet of multimedia things
• algorithmic optimization
• computational complexity reduction
• AI implementation challenges
• image processing