Engineering Commons^™

High-Performance Learning Systems Using Low-Precision Nanoscale Devices, Nandakumar Sasidharan Rajalekshmi

Dissertations

Brain-inspired computation promises a paradigm shift in information processing, both in terms of its parallel processing architecture and the ability to learn to tackle problems deemed unsolvable by traditional algorithmic approaches. The computational capability of the human brain is believed to stem from an interconnected network of 100 billion compute nodes (neurons) that interact with each other through approximately 10¹⁵ adjustable memory junctions (synapses). The conductance of synapses is modifiable allowing the network to learn and perform various cognitive functions. Artificial neural networks inspired by this architecture have demonstrated even super-human performance in many complex tasks.

Computational systems based …

Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue

Dissertations

Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …