Deep learning has revolutionised a breadth of industries by automating critical tasks while achieving superhuman accuracy. However, many of these benefits are driven by huge neural networks deployed on cloud servers that consume enormous energy. This thesis contributes two classes of novel frameworks and algorithms that extend the deployment frontier of deep learning models to tiny edge devices, which commonly operate in noisy environments with limited compute footprints:
(1) New frameworks for efficient edge AI. We introduce methods that reduce inference cost through filter pruning and efficient network design. CUP presents a new method for compressing and accelerating models, by clustering and pruning similar filters in each layer. CMPNAS presents a new visual search framework that optimises a small and efficient edge model to work in tandem with a large server model to achieve high accuracy, achieving up to 80x compute cost reduction.
(2) New methods for robust edge AI. We Introduce new methods that enable robustness to real-world noise while reducing inference cost. REST extends the scope of pruning to obtain networks that are 9x more efficient, run 5x faster and robust to adversarial and gaussian noise. HAR generalises the idea of early exiting in multi-branch neural networks to the training phase leading to networks that obtain state-of-the-art accuracy under class imbalance while saving up to 20% inference compute. IMBNAS optimises neural architectures on imbalanced datasets through super-network adaptation strategies that lead to 5x compute savings compared to searching from scratch.
Our work makes a significant impact to industry and society: CMPNAS enables the edge deployment use-case for fashion and face retrieval services, and was highlighted at Amazon company-wide to thousands of researchers and developers. REST enables at-home sleep monitoring through a mobile phone and was highlighted by several news media.Ph.D
