In principle, sparse neural networks should be significantly more efficient than traditional
dense networks. Neurons in the brain exhibit two types of sparsity; they are sparsely …

Deep neural networks typically have extensive parameters and computational operations.
Pruning and quantization techniques have been widely used to reduce the complexity of …

Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine
Learning (ML) and have achieved outstanding results in data-intensive applications, such as …

Deep learning has become popular in recent years primarily due to powerful computing
devices such as graphics processing units (GPUs). However, it is challenging to deploy …

Context. The problem of optimizing the resilience of artificial intelligence systems to
destructive disturbances has not yet been fully solved and is quite relevant for safety-critical …

Artificial intelligence technologies are becoming increasingly prevalent in resource-
constrained, safety-critical embedded systems. Numerous methods exist to enhance the …

Neural network pruning has been deemed essential in the deployment of deep neural
networks on resource-constrained edge devices, greatly reducing the number of network …

The problem of optimizing the resilience of image recognition systems to destructive
disturbances has not yet been fully solved and is quite relevant for safety-critical …

Spiking neural network (SNN), due to its event-based nature, has gained popularity in the
recent years as an energy-efficient algorithm to service the applications that are restricted on …

Deep learning has become popular in recent years primarily due to powerful computing
devices such as GPUs. However, many applications such as face alignment, image …