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

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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.