Abstract
In this paper, an efficient FPGA-based architecture for Extended Associative Memories (EAM) focused on the classification stage of an image recognition system for real-time applications is presented. The EAM training phase is only used during the generation of associative memory, completed this task, this module is disconnected from the system; for this reason the hardware architecture of this module was designed for optimize the FPGA resource usage. On the other hand, the EAM can be part of a system requiring working in real time, such a perception system for a mobile robot or a personal identification system; for this reason, the hardware architecture of EAM classification phase was designed for obtaining high processing speeds. Experimental results show high performance of our proposal when altered versions of the images used to train the memory are presented.
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Enrique, GR., Ignacio, AF., Carlos, GR., Jesús, LF., Oleksiy, P. (2013). FPGA-Based Architecture for Extended Associative Memories and Its Application in Image Recognition. In: Batyrshin, I., González Mendoza, M. (eds) Advances in Artificial Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37807-2_17
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