All-optical associative learning element - Tan, JYS (2021)
This thesis examines the idea that Pavlovian associative learning may be a building block in neural networks. With emphasis on hardware acceleration, on-chip optical associative learning device is conceived as an alternative to artificial neural networks (ANNs) hardware for high-speed applications. It is well known that ‘conventional’ ANNs, particularly in the form of modern deep neural networks (DNNs), are usually carried out using the backpropagation method. In the method, network weights are updated such that the net output better resembles the desired output after each forward-backward iteration, until convergence is met. A distinct approach is presented using associative learning as the basis of AI learning process. In formulating the architecture, associative learning is linked with supervised learning, based on their common goal of associating certain inputs with ‘correct’ outputs. The intuition leads to a reductionist framework to the problem, facilitating the transition from a single (or monadic) Pavlovian single Input-Teacher association to any arbitrary n Input-Teacher associations.