  Convolutional Neural Networks (CNNs) have greatly influenced the field of
Embedded Vision and Edge Artificial Intelligence (AI), enabling powerful
machine learning capabilities on resource-constrained devices. This article
explores the relationship between CNN compute requirements and memory bandwidth
in the context of Edge AI. We delve into the historical progression of CNN
architectures, from the early pioneering models to the current state-of-the-art
designs, highlighting the advancements in compute-intensive operations. We
examine the impact of increasing model complexity on both computational
requirements and memory access patterns. The paper presents a comparison
analysis of the evolving trade-off between compute demands and memory bandwidth
requirements in CNNs. This analysis provides insights into designing efficient
architectures and potential hardware accelerators in enhancing CNN performance
on edge devices.
