IP on AP: Exploring Image Processing on the Automata Processor

The Automata Processor is a novel hardware accelerator that can perform pattern matching in parallel. To date, this pattern matching has been limited to one-dimensional problems that can be implemented as flexible string-matching methods such as those found in genomics. In this thesis, we present a novel process of implementing image retrieval using a multinary representation for deployment on an automata framework. Images are encoded into discriminative and unique regular expression descriptors in such a way that can be used for classification purposes. The regular expression descriptors are streamed through sets of non-deterministic finite automata (NFA).

To improve performance of this multi-dimensional classification problem, we transform discriminative feature descriptors using a cumulative distribution transform. The transformed features are encoded into regular expressions which can be executed on the automata processor.
The thesis also highlights methods of evaluating the similarity between images using these regular expressions in the automata processor. Our image retrieval and classification method improves on classification accuracy and achieves a run-time of less than one one-hundredth of a second per image which represents a three-fold improvement over competing architectures.