Novel Neuromorphic Mechanisms and Structures

新颖的神经形态机制和结构

• 批准号: RGPIN-2020-07108
• 负责人: Sylvestre, Julien
• 金额: $ 1.97万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741597
• 关键词: Novel; Neuromorphic; Mechanisms; Structures

The objective of the proposed research program is to develop novel mechanical devices using concepts similar to those which have so successfully been used with artificial neural networks in the field of machine learning. By adapting to physical objects system architectures and design methodologies that were initially developed for machine learning in software, the proposed research will lead to entirely new classes of mechanical devices which can implement complex functions, be simple to design in an automated manner, and be highly efficient in terms of size and energy consumption.     Over the last few years, we have contributed to the understanding that some of the most fundamental features of artificial neural networks, which enable their computing model and lead to their advantageous properties, can actually be realized in physical objects, and in particular in mechanical systems. As an example, we have shown that the non-linear dynamics of a small silicon beam clamped at both ends in a MEMS can be used as a resource for energy-efficient, dense neuromorphic computations. We have also presented the first demonstration of a 3D-printed metamaterial with a stiffness that is a complex function of patterns in the external force field acting on the metamaterial, and which can therefore be trained to respond in highly specific manners to external loads.     The proposed work consists in the systematic investigation of the physical implementation of machine learning concepts directly within mechanical systems and structures. We have already demonstrated that this line of research could yield functional prototypes which represent a new way of building physical devices, to provide solutions for challenging applications. With this Discovery grant, various concepts from the field of machine learning will be applied to mechanical objects that are designed to have certain properties that are similar to those found in artificial neural networks. As a result, the mechanical devices will have the ability to respond in elaborated ways to external loads or stimuli (acceleration, sound). They will be trained to acquire these complex responses, instead of being designed to the smallest detail. And they are expected to inherit the remarkable generalization capability of neural networks, to respond adequately to stimuli never seen during training. The main anticipated outcome of the proposed research will be an analysis and design methodology supporting new classes of devices (MEMS, metamaterials, etc.). In the long term, these could be transferred to the industry to more efficiently solve problems in high technology fields such as patient health monitoring, robot control, automated manufacturing, smart sensors and the Internet of Things.

拟议研究计划的目标是通过适应最初开发的物理对象系统架构和设计方法，使用类似于机器学习领域中人工神经网络成功使用的概念来开发新型机械设备。通过软件中的机器学习，拟议的研究将带来全新类别的机械设备，这些设备可以实现复杂的功能，以自动化的方式进行简单的设计，并且在尺寸和能耗方面非常高效。 ，我们促成了这样的理解：一些人工神经网络的最基本特征使其计算模型得以实现并产生其有利的特性，这些特征实际上可以在物理对象中实现，特别是在机械系统中，例如，我们已经证明了非线性动力学。我们还首次展示了 3D 打印超材料，其刚度是 MEMS 中图案的复杂函数。外力场作用于超材料，因此可以被训练以高度特定的方式响应外部负载。这项工作包括直接在机械系统和结构中研究机器系统学习概念的物理实现。产生功能原型，代表一种构建物理设备的新方法，为具有挑战性的应用提供解决方案。通过这项发现资助，机器学习领域的各种概念将应用于被设计为具有类似于的某些属性的机械对象。那些在人工神经网络中发现的。结果，机械设备将有能力以复杂的方式响应外部负载或刺激（加速度、声音），它们将被训练来获得这些复杂的响应，而不是被设计为最小的细节。神经网络卓越的泛化能力，能够对训练期间从未见过的刺激做出充分的反应。从长远来看，该研究的主要预期成果将是支持新型设备（MEMS、超材料等）的分析和设计方法。条件是，这些可以转移到行业更高效地解决患者健康监测、机器人控制、自动化制造、智能传感器和物联网等高科技领域的问题。