On-chip template training system and image processing applications using iterative annealing on ACE16k chip

Cellular neural networks proved to be a useful parallel computing system for image processing applications. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. CNN templates are the key parameters to perform a desired task. One of the challenging problems in designing templates is to find the optimal template that functions appropriately for the solution of the intended problem. In this paper, we have implemented the Iterative Annealing Optimization Method on the analog CNN chip to find an optimum template by training a randomly selected initial template. We have been able to show that the proposed system is efficient to find the suitable template for some specific image processing applications.

► CNN is a useful parallel computing system for image processing applications.
► ACE16K is an array of 128 × 128 identical, locally interacting, analog processor.
► Iterative Annealing technique is implemented using ACE16k CNN chip in Bi-i.
► Robust corner detection templates have been trained for ACE16k chip.
► Results show that ACE16k is fast and suitable for image processing applications.