A memory-based recurrent neural architecture for chips emulating cortical visual processing

The paper describes the architecture and the simulated performances of a memory-based chip that emulates human cortical processing in early visual tasks, such as texture segregation. The featural elements present in an image are extracted by a convolution block and subsequently processed by the cortical chip, whose neurons, organized into three layers, gain relational descriptions (intelligent processing) through recurrent inhibitory/excitatory interactions between both inter- and intra-layer parallel pathways. The digital implementation of this architecture directly maps the set of equations determining the status of the cortical network to achieve an optimal exploitation of VLSI technology in neural computation. Neurons are mapped into a memory matrix whose elements are updated through a programmable computational unit that implements synaptic interconnections. By using 0.5um-CMOS technology, full cortical image processing can be attained on a single chip (20x20 mm^2 die) at a rate higher than 70 frames/second, for images of 256x256 pixels.