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

A. Parihar, A. Anvesha, M. Jerry, S. Datta, A. Raychowdhury
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引用次数: 0

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.
耦合系统动力学及其计算特性
集体动力系统通过利用振荡器或尖峰发生器等简单元素的复杂相互作用,为计算提供了独特的机会。当这种动态可以被编程、控制和观察时,这是可能的。在这次演讲中,我们将介绍我们的一些工作,我们正在探索确定性和随机动力系统在CMOS和后CMOS计算基板中的时间演化。这样的系统在解决逆问题、分布式优化(凸优化和组合优化)和机器学习中得到了应用。在本文中,我们将讨论我们最近的工作,连接动力学和代数图论。我们将讨论这种动态在混合信号CMOS中的实现,包括最近对能量受限边缘设备的强化学习的演示。最后,我们将简要讨论实现这种计算系统的机会、潜力和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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