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: 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=716635
• 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) 技术自动调整在射频 (RF) 下运行的模拟电路的大小。人工智能最近的成功在很大程度上是由于存在大量用于训练的历史数据，并且主要需要考虑一对一的关系。这些特征在工程设计中是非典型的，其中的问题通常是不确定的，并且训练示例很少。还要求解决方案能够解释透明度、信任和帮助决策，并且在电子设计自动化 (EDA) 的情况下，它们需要考虑隐式设计约束。 拟议的研究将详细阐述一个解决这些问题的模拟射频电路自动调整大小的框架。将开展三项主要活动： 1. 创建一个高效的人工神经网络 (ANN) 架构，用于自动合成 RF 电路和确定组件尺寸，该架构准确，可以从少数示例中学习并处理组件耦合等隐式约束。 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来补偿实现效果，例如布局、阻抗失配和 RF 电路尺寸的可变性问题。 3. 开发一种方法，根据白盒操作的领域知识来解释所创建的 ANN 架构的操作，更容易进行超参数调整，并允许专家在调整过程中输入 前面的任务将通过神经网络、进化优化算法和模糊逻辑的结合来完成。为了降低 ANN 的复杂性以降低训练要求，将研究稀疏计算技术，例如可变大小分区和部分计算，为了实现现实世界的效果问责，进化优化算法将以 ANN 输出作为初始解决方案，并使用代理模式技术来实现快速收敛，并且为了可解释性，将使用具有领域知识增强的输入输出关系的模糊提取。 创建的设计方法为模拟电路设计的声明性方法和更快的电路设计打开了大门。鉴于无线设备的需求不断增加，以及模拟 RF 前端的复杂性不断增加，EDA 工具正变得必不可少。此外，它可以很容易地适应除拟议研究中解决的问题之外的其他非线性设计问题。最后，参与这项研究的学生将在工程、人工智能和认知信息学的帮助下学习多学科技能。因此，该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先地位。