Collaborative Research: Seer: A Gigascale Neuromorphic Visual System

Stanford University, Stanford CA

Investigators

Abstract

The goal of this project is to parse video from a moving platform in real-time to produce retinotopic maps that reveal the spatial layout of the scene as well as any independently moving objects present. The project proposes to duplicate the function of the early visual system in a multichip neuromorphic system with two hundred and thirty thousand silicon neurons (three quarters the number of pixels in a VGA image) and three billion synaptic interactions several orders of magnitude larger than anything built to date. The outcome of this project will be SEER, a subwatt, paperback-size seeing machine. The intellectual merit of the proposed research stems from the tight synergy between the computational theory, based on the principle of compositionality ,and the neuromorphic implementation, based on reentrant networks. Compositionality dictates that the various parts of the vision problem should be attacked simultaneously and reentrancy gives us the capability to do exactly that. The broader impact of the proposed effort could be enormous. A multichip neuromorphic system performing multimodal segmentations would reinvigorate robotics and computer vision. By providing the infrastructure for early vision, it will facilitate the study of cognition, which will most likely generate a flood of new theories and experiments, in the Neurosciences and in the sciences of the artificial.