Neuromorphic Vision Hybrid RRAM-CMOS Architecture

The development of a bioinspired image sensor, which can match the functionality of the vertebrate retina, has provided new opportunities for vision systems and processing through the realization of new architectures. Research in both retinal cellular systems and nanodriven memristive technology has made a challenging arena more accessible to emulate features of the retina that are closer to biological systems. This paper synthesizes the signal flow path of photocurrent throughout a retina in a scalable 180-nm CMOS technology, which initiates at a <inline-formula> <tex-math notation="LaTeX">$128\times 128$ </tex-math></inline-formula> active pixel image sensor, and converges to a <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> array, where each node emits a spike train synonymous to the function of the retinal ganglionic output cell. This signal can be sent to the visual cortex for image interpretation as part of an artificial vision system. Layers of memristive networks are used to emulate the functions of horizontal and amacrine cells in the retina, which average and converge signals. The resulting image matches biologically verified results within an error margin of 6% and exhibits the following features of the retina: lateral inhibition, asynchronous adaptation, and a low-dynamic-range integration active pixel sensor to perceive a high-dynamic-range scene.