Real-Time Trainable Data Converters for General Purpose Applications

Abstract

Data converters are ubiquitous in data-abundant systems, where they are heterogeneously distributed across the analog-digital interface. Unfortunately, conventional data converters trade off speed, power, and accuracy. Furthermore, intrinsic real-time and post-silicon variations dramatically degrade their performance. In this paper, we employ novel neuro-inspired approaches to design smart data converters that could be trained in real-time for general purpose applications, using machine learning algorithms and artificial neural network architectures. Our approach integrates emerging memristor technology with CMOS. This concept will pave the way towards adaptive interfaces with the continuous varying conditions of data driven applications.