Due to their size, the capability of these networks has increased tremendously, but this has come at the cost of a significant increase in necessary compute.

Implicit Neural Representations (INRs) have previously been used to represent and compress image and video content, demonstrating relatively high decoding speed compared to other methods.

Our proposed method significantly reduces communication overhead in federated learning. This method poses a trade-off between time and memory complexity. We also provide detailed information about the optimization hyperparameters e.g. In this section, we explore the effect of fitness sparsification i.e. selecting top-k fitness values from the To enable a fair and insightful comparison between the two population sizes, our focus was on assessing performance based on the number of members remaining post-sparsification rather than directly contrasting sparsification rates. Our results underline the crucial role that population size plays in exploring optimal solutions, overshadowing even the significance of compression rate.

In this paper, we set out to answer the question on which is better: neural network quantization or pruning? By answering this question, we hope to inform design decisions made on neural network hardware going forward. We provide an extensive comparison between the two techniques for compressing deep neural networks.

Weight-sharing quantization has emerged as a technique to reduce energy expenditure during inference in large neural networks by constraining their weights to a limited set of values.

In addition, we demonstrate the scheme's satisfactory architecture generality by providing QuantSR-C