Integration of ULP neurons network based on Injection Locked Oscillators
Published : 8 February 2020
Neural Networks have demonstrated their superiority compared to Von Neumman computing machines for complex classification tasks. Embedding neural networks near the sensors (Edge IA) is a promising way to afford decision autonomy to sensor nodes. This could lead to a global decrease of the power consumption of sensor networks by decreasing the information rate between the nodes and the calculation center which will have also to provide a smaller amount of calculation.
Decreasing the power consumption of neurones is a hot research topic as it is a key toward Edge IA. Beside digital implementations, some analog implementations are proposed, but these solutions are bulky and their power consumption is still high. The aim of the thesis work is to demonstrate the feasibility of the implementation of a neural network using Ultra Low Power Injection Locked Oscillators as neurones. Thesis work should lead to the silicon demonstration of learning ability of such networks.
Applicant should have a good knowledge of statistical learning and neural networks in particular. He should have good knowledge of analog electronics. Theoretical study will necessitate strong expertise on both mathematics and modelling using python