A context switching streaming memory architecture to accelerate a neocortex model
Visual attention systems inspired by the behavior of neural architectures have attracted the attention of many researchers in the computer vision field. Of special interest is the model proposed by Li where the bottom-up saliency features of an image are detected through a mechanism that simulates the operation of the primary visual cortex (VI). Beyond its biological nature, the specific model is also of interest because it performs texture segmentation and contour enhancement using the same circuitry. The main drawback of the proposed model is its computational complexity, making it time consuming to simulate the model in software to, e.g., explore the model parameters, and also limits its applicability in real-time scenarios. In this work, we explore the inherent parallelism that exists in the model and propose a flexible hardware architecture that can accelerate the model. More over, the flexibility of the proposed architecture to adapt to similar models of the brain is of significant concern. Performance evaluation shows that the proposed architecture gives results close to the software model, achieving at the same time a speed up of one order of magnitude.