This sparse but high-yield data is received by the brain for additional processing and eventual perception. While cameras are mostly light detectors, the eyes are sophisticated analysts, processing and extracting clues. So how does it work? The answer is again simple: eyes are specialized sensors which extract and transfer only the “relevant” information (vision intelligence), rather than taking snapshots or videos to store or send to the brain. However, the eye-brain “data link” has a maximum capacity of 10 Mbits/sec. The eye has ~100x more resolution, and if it were operated like a camera it would transfer ~600 Gb/s to the brain. To compare, we need to first examine the eyes and brain and the architecture connecting them. off-the-self cameras and processors), it becomes difficult to integrate into a flexible solution and more importantly dynamically optimize in real-time, a key aspect of human vision. When a solution is developed from existing components (i.e. custom chips), driven by demand from applications which may see benefit in specialized implementations such as image processing.Īnother important disconnect, albeit less obvious, is the architecture itself. The latest trend is domain specific architecture (i.e.
Processors have evolved over time with the primary performance measure being operations per second. Cameras were built for accurate communication and reproduction. So what is the problem? The answer is surprisingly simple:Ĭameras and processors operate very differently relative to the human eye and brain, largely because they were historically developed for different purposes. We remain a far cry from replicating the efficacy and speed of human vision. This creates a fundamental tradeoff between time and energy, and most solutions optimize one at the expense of the other. It is the combination of the time and energy “wasted” in extracting the required information from the captured signal that is to blame for this inefficiency. However, current technology is as high as 40,000x behind, particularly in terms of efficiency. With mega-pixels of resolution and trillions of operations per second (TOPS), one would expect vision architecture (camera + computer) today to be on par with human vision. The eye is a critical component, which explains the predominance of cameras in AI.
Rizk discusses the hypothesis that he and his team have developed: Efficient Vision Intelligence (VI) is a prerequisite for effective Artificial Intelligence (AI) for edge applications and beyond.ĭespite astronomical advances, human vision remains superior to machine vision and is still our inspiration. Charbel Rizk, the Founder & CEO of Oculi® - a spinout from Johns Hopkins - a fabless semiconductor startup commercializing patented technology to address the huge inefficiencies with vision technology. Author: This article is part of an expert piece written by Dr.
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