Novel Neuromorphic Mechanisms and Structures

拟议的研究计划的目标是开发新的机械设备，其概念类似于机器学习领域中已成功与人工神经网络一起使用的概念。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体系结构和设计方法，拟议的研究将导致全新的机械设备类别，这些设备可以执行复杂的功能，以自动化方式简单设计，并在尺寸和能源消耗方面高效。在过去的几年里，我们帮助理解了人工神经网络的一些最基本的功能，这些功能使其能够建立计算模型，并导致其优势特性，实际上可以在物理对象中实现，特别是在机械系统中。作为一个例子，我们已经证明，MEMS中两端固定的硅小梁的非线性动力学可以用作高能效、密集的神经形态计算的资源。我们还展示了3D打印超材料的第一个演示，其硬度是作用于超材料的外力场中图案的复杂函数，因此可以训练它以高度特定的方式对外部载荷做出反应。他说，拟议的工作包括对直接在机械系统和结构内物理实施机器学习概念进行系统调查。我们已经证明，这一系列研究可以产生功能原型，这代表了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。有了这项发现基金，机器学习领域的各种概念将被应用于机械对象，这些对象被设计为具有与人工神经网络中发现的属性相似的某些属性。因此，机械设备将具有以精心设计的方式对外部负载或刺激(加速度、声音)做出反应的能力。他们将接受训练，以获得这些复杂的反应，而不是被设计成最小的细节。他们有望继承神经网络非凡的泛化能力，对训练中从未见过的刺激做出充分反应。拟议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。从长远来看，这些可以转移到行业，以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。

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.