Advanced Intelligent Systems · 2019 · 55 citations · 95 references
ComputingComputer ScienceNeuroscienceArtificial IntelligenceMachine VisionVisual PerceptionImage ProcessingObject RecognitionSpiking Neural NetworksVisual SystemBionic Vision SystemsComputational VisionNeural Network ModelingSub-nanosecond TimingSynaptic LayersVisual Signal Processing
Current bionic/robotic eye systems are bulky multipart assemblies, but recent advances in artificial synaptic devices are moving toward hardware‑based deep learning that could perform image processing in a single clock cycle. The authors propose an artificial cogni‑retina—a millimeter‑scale intelligent device—to replace bulky systems and execute simple image‑processing tasks. The device connects directly to the optic nerve, acting as a bionic limb that perceives, learns, remembers, and classifies elementary visual data. Theoretical analysis indicates this system offers a quantum leap in size, power consumption, and speed for prosthetic eyes and autonomous vehicle vision.
The state‐of‐the‐art conception of a bionic/robotic eye is a somewhat bulky multipart system comprising a video camera connected to a processing unit that in turn communicates data through a wireless transmitter to either an in vivo retinal implant or a computer system. An artificial cogni‐retina is a millimeter‐scale, intelligent apparatus designed as a replacement for these systems, while executing simple image processing tasks. As a bionic limb, it can connect directly to the optic nerve and perform rudimentary cognitive functions such as perceiving, learning, remembering, and classifying elementary visual data. This theoretical system presents a quantum leap in terms of size, power consumption, and speed to both prosthetic human eyes and robotic vision in artificial intelligence‐based platforms such as autonomous vehicles. Recently, an increasing number of publications have used interesting materials in artificial synaptic devices that drive this idea closer toward becoming a real‐world application. Such devices may form a basis for hardware‐based deep learning artificial neural networks that can potentially execute image processing tasks within a single clock cycle compared to software algorithms running on conventional von Neumann machines that require millions of cycles to perform image sensor interfacing, memory fetch operations, and data path propagation.
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