Dynamics of Coupled Systems and their Computing Properties Invited Paper : Invited Paper

Abstract

Collective dynamical systems offer unique opportunities for computing by harnessing the complex interactions of simple elements such as oscillators or spike generators. This is possible when such dynamics can be programmed, controlled, and observed. In this talk, we will present some of our work where we are exploring the timeevolution of both deterministic and stochastic dynamical systems in both CMOS and post-CMOS computing substrates. Such systems find applications in solving inverse problems, distributed optimizations (convex and combinatorial) and machine learning. In this paper we will discuss our recent work that connects dynamics and algebraic graph theory. We will talk about implementation of such dynamics in mixed-signal CMOS, including a recent demonstration of reinforcement learning for energy-constrained edge devices. We will conclude with a brief discussion of the opportunities, potentials and challenges in realizing such computational systems.