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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717906
• 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开发新型机械装置，使用类似于机器学习领域中成功用于人工神经网络的概念。通过适应最初为软件中的机器学习而开发的物理对象系统架构和设计方法，拟议的研究将导致全新的机械设备类别，这些设备可以实现复杂的功能，以自动化的方式简单设计，并且在尺寸和能耗方面非常高效。