**Abstract**

Reverse-engineering the human brain has been a grand challenge for researchers in machine learning, experimental neuroscience, and computer architecture. Current deep neural networks (DNNs), motivated by the same challenge, have achieved remarkable results in Machine Learning applications. However, despite their original inspiration from the brain, DNNs have largely moved away from biological plausibility, resorting to intensive statistical processing on huge amounts of data. This has led to exponentially increasing demand on hardware compute resources that is quickly becoming economically and technologically unsustainable. Recent neuroscience research has led to a new theory on human intelligence, that suggests Cortical Columns (CCs) as the fundamental processing units in the neocortex that encapsulate intelligence. Each CC has the potential to learn models of complete objects through continuous predict-sense-update loops. This leads to the overarching question: *Can we build Cortical Columns Computing Systems (C3S) that possess brain-like capabilities as well as brain-like efficiency?* This chapter presents ongoing research in the Neuromorphic Computer Architecture Lab (NCAL) at Carnegie Mellon University (CMU) focusing on addressing this question. Our initial findings indicate that designing truly intelligent and extremely energy-efficient C3S-based sensory processing units, using off-the-shelf digital CMOS technology and tools, is quite feasible and very promising, and certainly warrants further research exploration.

**Keywords:** neuromorphic computing, neocortical columns, spiking temporal neural networks, online continual learning, intelligent sensory processing
