Towards Hardware Realizations of Intelligent Systems: A Cortical Column Approach

Many researchers seek for alternatives to traditional computing architectures, often placing emphasis on modeling biological systems that possess intelligence and learning capabilities. Cortical columns have emerged as a high-level unsupervised learning model for extracting independent data features in a hierarchical manner. Previous cortical models simply rely on software based techniques and neglect the actual purpose of investigating these architectures: to diverge from the conventional Von Neumann approach to an actual hardware realization of an intelligent system. This work presents the hardware realization of a cortical column system, taking several factors into consideration which were previously disregarded by software models. We introduce a Neural Spike Dual-Rail communication scheme, and a temporal pooling unit capable of detecting data distortions during training and testing. This work concludes with a study on cortical columns and their hierarchical impact on hardware resources.