Invited: A Cross-layer Approach to Cognitive Computing

Abstract

Remarkable advances in machine learning and artificial intelligence have been made in various domains, achieving near-human performance in a plethora of cognitive tasks including vision, speech and natural language processing. However, implementations of such cognitive algorithms in conventional "von-Neumann"architectures are orders of magnitude more area and power expensive than the biological brain. Therefore, it is imperative to search for fundamentally new approaches so that the improvement in computing performance and efficiency can keep up with the exponential growth of the AI computational demand. In this article, we present a cross-layer approach to the exploration of new paradigms in cognitive computing. This effort spans new learning algorithms inspired from biological information processing principles, network architectures best suited for such algorithms, and neuromorphic hardware substrates such as computing-in-memory fabrics in order to build intelligent machines that can achieve orders of improvement in energy efficiency at cognitive processing. We argue that such cross-layer innovations in cognitive computing are well-poised to enable a new wave of autonomous intelligence across the computing spectrum, from resource-constrained IoT devices to the cloud.