Design Close to the Edge for Advanced Technology using Machine Learning and Brain-Inspired Algorithms

Abstract

In advanced technology nodes, transistor performance is increasingly impacted by different types of design-time and run-time degradation. First, variation is inherent to the manufacturing process and is constant over the lifetime. Second, aging effects degrade the transistor over its whole life and can cause failures later on. Both effects impact the underlying electrical properties of which the threshold voltage is the most important. To estimate the degradation-induced changes in the transistor performance for a whole circuit, extensive SPICE simulations have to be performed. However, for large circuits, the computational effort of such simulations can become infeasible very quickly. Furthermore, the SPICE simulations cannot be delegated to circuit designers, since the required underlying transistor models cannot be shared due to their high confidentiality for the foundry. In this paper, we tackle these challenges at multiple levels, ranging from transistor to memory to circuit level. We employ machine learning and brain-inspired algorithms to overcome computational infeasibility and confidentiality problems, paving the way towards design close to the edge.