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

神经形态计算和其他受自然启发的硬件和软件设计方法

• 批准号: RGPIN-2019-07217
• 负责人: Boukadoum, Mounir
• 金额: $ 2.04万
• 依托单位: Université du Québec à Montréal
• 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=739367
• 关键词: Neuromorphic; computing; other; nature; inspired

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.

这项研究涉及使用人工智能(AI)技术自动调整工作在射频(RF)的模拟电路的大小。人工智能最近的成功在很大程度上是因为存在大量用于训练的历史数据，以及主要需要考虑的一对一关系。这些特征在工程设计中是不典型的，在工程设计中，问题往往是不确定的，训练例子也很少。还要求解决方案具有透明度、信任和有助于决策的可解释性，在电子设计自动化(EDA)的情况下，解决方案应考虑到隐含的设计限制。拟议的研究将为解决这些问题的模拟射频电路自动调整大小制定框架。主要工作有三个方面：1.建立一个高效的人工神经网络(ANN)结构，用于射频电路的自动综合和元件尺寸确定，该结构准确，可以从几个例子中学习，并处理元件耦合等隐式约束。2.找到一种有效的优化算法来补偿版图、阻抗失配和变异性等实现对射频电路尺寸的影响，使用所创建的人工神经网络的输出作为初始开始。3.开发一种方法，根据白盒操作的领域知识、更容易的超参数调整，并在调整过程中允许专家输入，来解释所创建的ANN体系结构的操作。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。为了在较低的训练要求下降低人工神经网络的复杂度，将研究可变大小划分和部分计算等稀疏计算技术；对于真实世界的效果问责，进化优化算法将以神经网络输出为初始解并使用代理模式技术来实现快速收敛；对于可解释性，将使用输入输出关系的模糊提取和领域知识增强。创建的设计方法为模拟电路设计的声明性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，以及其模拟射频前端的复杂性不断增加，EDA工具正变得势在必行。此外，它还可以很容易地适用于其他非线性设计问题，而不是本文所讨论的问题。最后，参与这项研究的学生将学习多学科技能，来自工程学、人工智能和认知信息学的投入。因此，这一研究计划也将有助于保持加拿大在整个人工智能领域的领先地位。