Analog Computing in a Modern Context: A Linear Algebra Accelerator Case Study

Abstract

Approaching the post-Moore's law era, researchers are looking for scalable ways to get useful computation from existing silicon technology. This article presents a programmable analog accelerator for solving systems of linear equations. The authors compensate for commonly perceived downsides of analog computing, such as low precision and accuracy, limited problem sizes, and difficulty applying it to different workloads. On the basis of a prototyped analog accelerator chip, they compare the analog solver's performance and energy consumption against an efficient digital algorithm running on a general-purpose processor. The analog accelerator approach is 10 times faster and provides 33 percent energy savings. Owing to the speed and efficiency of linear algebra algorithms running on digital computers, an analog accelerator that matches digital performance needs a large silicon footprint, which limits scalability. The authors conclude that problem classes outside of systems of linear equations could hold more promise for analog acceleration.