论文标题
重新启动神经形态硬件设计 - 复杂性工程方法
Rebooting Neuromorphic Hardware Design -- A Complexity Engineering Approach
论文作者
论文摘要
随着机器学习和人工智能应用程序的计算需求继续增长，神经形态硬件已被吹捧为潜在的解决方案。新的新兴设备（例如Memristors，Atomic Switches等）显示出替换基于CMOS电路的巨大潜力，但在设备可变性，随机行为和可扩展性方面受到了多个挑战的阻碍。在本文中，我们将介绍一个描述<->设计框架，以分析过去在计算方面的成功，了解当前问题并确定向前发展的解决方案。具有这些新兴设备的工程系统可能需要修改我们将为设计的学习类型以及我们采用的设计方法，以实现这些新描述。我们将探索复杂性工程的想法，并通过新颖的计算织物来分析它们与传统的神经形态设计方法相比的优势和挑战。水库计算示例用于了解朝着复杂性工程方法迈进的特定变化。时间是重大重新启动我们的设计方法和成功的理想时间，这将代表神经形态硬件的设计方式的根本转变，并为新范式铺平了道路。
As the compute demands for machine learning and artificial intelligence applications continue to grow, neuromorphic hardware has been touted as a potential solution. New emerging devices like memristors, atomic switches, etc have shown tremendous potential to replace CMOS-based circuits but have been hindered by multiple challenges with respect to device variability, stochastic behavior and scalability. In this paper we will introduce a Description<->Design framework to analyze past successes in computing, understand current problems and identify solutions moving forward. Engineering systems with these emerging devices might require the modification of both the type of descriptions of learning that we will design for, and the design methodologies we employ in order to realize these new descriptions. We will explore ideas from complexity engineering and analyze the advantages and challenges they offer over traditional approaches to neuromorphic design with novel computing fabrics. A reservoir computing example is used to understand the specific changes that would accompany in moving towards a complexity engineering approach. The time is ideal for a significant reboot of our design methodologies and success will represent a radical shift in how neuromorphic hardware is designed and pave the way for a new paradigm.
