Neuromorphic computing and other nature-inspired methods for hardware and software design

本研究主要探讨利用人工智慧技术，自动调整射频类比电路的尺寸。人工智能最近的成功在很大程度上是由于存在大量的历史数据用于训练，并且主要考虑一对一的关系。这些特征在工程设计中是非典型的，在工程设计中，问题往往是不确定的，训练样本很少。还有一个要求，即解决方案是可解释的透明度，信任和帮助决策，并在电子设计自动化（EDA）的情况下，他们考虑到隐含的设计约束。 拟议的研究将详细阐述一个框架，用于解决这些问题的模拟RF电路的自动调整。主要有三个方面的工作：1.建立一个有效的人工神经网络（ANN）结构，用于RF电路的自动综合和元件尺寸的确定，该结构是准确的，可以从很少的例子中学习，并处理隐含的约束，如元件耦合。2.使用创建的ANN的输出作为初始启动，找到一种有效的优化算法，以补偿RF电路尺寸的实现效果，如布局、阻抗失配和可变性问题。3.开发一种方法来解释所创建的人工神经网络架构的白盒操作，更容易的超参数调整，并允许专家输入的领域知识方面的操作在规模调整过程中，前面的任务将通过神经网络，进化优化算法和模糊逻辑的组合来完成。为了降低人工神经网络的复杂性，较低的训练要求，稀疏计算技术，如可变大小的分区和部分计算将被调查，为现实世界的影响问责制，进化优化算法将采取人工神经网络的输出作为初始解决方案，并使用代理模式技术，以实现快速收敛，和可解释性，模糊提取的输入-输出关系与领域知识增强将被使用。 所创建的设计方法为模拟电路设计的声明式方法和这些电路的更快设计打开了大门。鉴于无线设备的需求不断增加，以及其模拟RF前端的复杂性不断增加，EDA工具正成为强制性的。此外，它可以很容易地适应其他非线性设计问题比在拟议的研究中解决的问题。最后，参加这项研究的学生将学习多学科技能，并获得工程、人工智能和认知信息学的输入。因此，这项研究计划也将有助于保持加拿大在人工智能整体领域的领导地位。

This research is concerned with the automatic sizing of analog circuits that operated at radio frequencies (RF) by using artificial intelligence (AI) techniques. The recent success of artificial intelligence is in big part due to the existence of large amounts of historical data for training, and of mainly one-to-one relationships to consider. Those features are atypical in engineering design, where the problems are often undetermined and the training examples few. There is also the requirement that the solutions be explainable for transparency, trust and help in decision-making, and in the case of electronic design automation (EDA), that they account for implicit design constraints. The proposed research will elaborate a framework for the automatic sizing of analog RF circuits that address those issues. Three main activities will be undertaken: 1.Create an efficient artificial neural network (ANN) architecture for the automatic synthesis and component sizing of RF circuits that is accurate and can learn from few examples and handle implicit constraints such as component coupling. 2.Find an efficient optimization algorithm to compensate for implementation effects such as layout, impedance mismatches and variability issues on the sizing of RF circuits, using the output of the created ANN as initial start. 3.Develop a methodology to interpret the operation of the created ANN architecture in terms of domain knowledge for white-box operation, easier hyperparameter tuning, and to allow for expert input during the sizing process The previous tasks will be accomplished through a combination of neural network, evolutionary optimization algorithm and fuzzy logic. To reduce ANN complexity for lower training requirements, sparse computing techniques such as variable-size partitioning and partial computations will be investigated, for real-world effects accountability, an evolutionary optimization algorithm will take the ANN output as initial solution and use surrogate mode techniques to achieve fast convergence, and for explainability, fuzzy extraction of input-output relationships with domain knowledge enhancement will be used. The created design methodology opens the door to a declarative approach to analog circuit design and the faster design of those circuits. Given the increasing needs of the wireless devices keeps increasing, and the ever-increasing complexity of their analog RF front ends, EDA tools are becoming mandatory. Moreover, it can be easily adapted to other nonlinear design problems than the one addressed in the proposed research. Finally, the students who take part in this research will learn multidisciplinary skills, with input from engineering, artificial intelligence and cognitive informatics. Thus, this research program will also contribute to maintain Canada's leader position in the overall field of artificial intelligence.