| الوصف: |
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. The hardware associative learning network is realised using silicon-based on-chip optical waveguides. Optical implementation of the learning network enables simultaneous parallel calculations using wavelength division multiplexing (WDM), which increases the capacity of AI information processing. This is adopted in the work described in this thesis. Monolithic integration of waveguide components, through which optical signals are channeled, augurs well for its adoption as artificial intelligence (AI) hardware accelerator. Machine learning using associative learning hardware network is demonstrated, with the network solving pattern and image recognition tasks. The patterns are randomised optical signals binary pattern, while the images are the discretised 72x72 patterns in the form of image pixels. Computational density of 118 TOPS/mm² is demonstrated, limited only by the available setup in the laboratory. The density is ... |
