VLSI implementations of CNNs for image processing and vision tasks: single and multiple chip approaches

Three alternative VLSI analog implementations of cellular neural networks (CNNs) are described and demonstrated with fabricated and tested chips, which have been devised to perform image processing and vision tasks: a programmable low-power CNN with embedded photosensors; a compact fixed-template CNN based on unipolar current-mode signals; and basic CMOS circuits to build an extended and biologically-inspired CNN model using spikes. The first two VLSI approaches are intended for focal-plane image processing applications. The third one allows, since its dynamics is defined by process-independent local ratios and its input/output can be efficiently multiplexed in time, the construction of very large multiple chip CNNs for more complex vision tasks.