High Performance Computing in Memristive Crossbars by Thermal-Heat Coupling (MemCouple)

尽管机器学习取得了令人印象深刻的成功，但人工智能(AI)的表现仍然比不上自然智能。由于他们不理解他们学到的东西，他们在认知任务上的工作效率更低。今天的机器学习运行在冯-诺依曼系列计算机上，这使得训练过程僵化和能量密集型，而人脑是一个巨大的平行的神经元网络，分为几个功能专门的区域，作为一个上下文相关的、自组织的和瞬时的子网络，每隔0.5到2个S，注意力的变化就会转移一次。然而，这导致了认知神经科学中最令人困惑的问题之一，众所周知，绑定问题，它与对网络动态及其连通性的相关性的理解，即对时空模式的理解，紧密地交织在一起。在这里，神经元的可塑性是导致突触权重的结构连接矩阵的关键因素，该矩阵决定了网络的整体功能。为了为认知电子学铺平道路，该项目旨在模拟记忆交叉杆阵列中时空模式的形成，并使其可用于非常规计算。为此，将利用热串扰效应。这应该通过调整记忆器件特性和纵横制结构特性来实现，以使它们满足局部学习规则。作为实验研究的补充，将为所制造的记忆器件开发具有可变性的紧凑模型，以及包括器件在空间和时间上的热耦合效应的阵列级模型。在模拟和实验验证的基础上，我们想要推导出能够最有效地利用热耦合效应的记忆交叉杆阵列的设计规则。为了确保记忆神经网络的最优训练性能，我们希望开发记忆交叉杆，通过合适的可塑性机制来实现局部学习机制，以产生时空模式并将系统动力学稳定在混沌的边缘。在这种临界状态下，神经网络在灵敏度、动态范围、相关长度、信息传递和敏感度方面具有最大的计算特性。因此，一个特别的目标是使用热串扰和泄漏电流来启动联想学习机制。最终的目标是一种神经形态系统，它以尽可能最低的电路开销工作，并具有最少数量的输入和输出神经元。

Despite the impressive successes in machine learning, artificial intelligence (AI) still not outperform natural ones. As they do not understand what they learn, they work more inefficiently on cognitive tasks. Today’s machine learning runs on serial von-Neumann computers making the training process inflexible and energy-intensive, whereas the human brain is a massive parallel network of neurons divided into functionally specialized regions participating as a context-dependent, self-organized, and transient subnetwork which is shifted by changes in attention every 0.5 to 2 s. This, however, leads to one of the most puzzling issues in cognitive neuroscience, well known as the binding problem, which is strongly interwoven with an understanding of the correlation of the network dynamics and its connectivity, i.e., the understanding of spatial -temporal patterns. Here, neuronal plasticity is a crucial factor leading to a structural connectivity matrix of synaptic weights that determines the overall function of the network. To pave the way to cognitive electronics this project aims to emulate the formation of temporal-spatial patterns in memristive crossbar arrays and to make them usable for unconventional computing. For this purpose, the effect of thermal crosstalk will be exploited. This should be achieved by tailoring the memristive device characteristics and crossbar structures properties so that they satisfy local learning rules. The experimental investigation will be complemented by developing variability-aware compact models for the fabricated memristive devices and array-level models that include thermal coupling effects between devices in space and time. Based on the simulations and the experimental validation, we want to deduce design rules for memristive crossbar arrays that enable the exploitation of thermal coupling effects in the most efficient way. To ensure optimal training properties for memristive neural networks we want to develop memristive crossbars that implement local learning mechanisms via suitable plasticity mechanisms to generate spatial-temporal patterns and stabilize the system dynamics at the edge of chaos. At this critical state neural networks have maximum computational properties in terms of sensitivity, dynamic range, correlation length, information transfer, and susceptibility. Therefore, a particular goal is to use thermal crosstalk and leakage currents to initiate associative learning mechanisms. The final objective is a neuromorphic system that works with the lowest possible circuit overhead and has a minimum number of input and output neurons.