Moving the Edge from Imaging to Vision

C. Rizk
FRIS Inc (dba Oculi), New York, United States

Keywords: Software-Defined Vision Sensor, Machine Vision, Sensing and Processing Unit, Edge Processing, Neuromorphic

In recent times, notable progress has been achieved in the field of artificial intelligence. These advancements have significantly enhanced the perception accuracy. However, performing these tasks still demands substantial computational power and memory resources, making it a resource-intensive endeavor. Consequently, power consumption and latency pose significant challenges for many systems operating in high-speed, edge applications. In partnership with GF, Oculi, a fabless spin-out of Johns Hopkins University, is commercializing a new architecture for computer/machine vision that is ideal for the edge. It enables fast, efficient, and secure 2D & 3D vision at any wavelength. The core product is the OCULI SPU, an intelligent and fully programmable vision sensor. It is the first integrated neuromorphic sensing and processing architecture. The OCULI SPU is capable of being dynamically configured to output select data in various modes. These modes include images or video, polarity events, smart events, and actionable information. Moreover, the SPU allows real-time programmability of spatial and temporal resolution, sensitivity, as well as dynamic range and bit depth. By enabling continuous optimization, computer/machine vision deploying the OCULI SPU, in lieu of imaging sensors, can reduce the latency-energy factor by more than 600x at a fraction of the cost.