TinyML Asia – Charbel Rizk: Oculi Enables 600x Reduction in Latency-Energy Factor for Visual Edge…

Oculi Enables 600x Reduction in Latency-Energy Factor for Visual Edge Applications by Moving Sensors from Imaging to Vision
Charbel RIZK,
Founder and CEO
Oculi

In recent times, notable progress has been achieved in the field of artificial intelligence, particularly in the extensive use of deep neural networks. These advancements have significantly enhanced the reliability of face detection, eye tracking, hand tracking, and people detection. 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 always-on, edge applications. The OCULI SPU represents an intelligent, programmable vision sensor 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, as well as dynamic range and bit depth. By enabling continuous optimization, computer/machine vision solutions 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. It’s important to note that the smart events and actionable information outputs and modes are distinctive features unique to Oculi. THE OCULI SPU is ideal for TinyML vision applications. To showcase these capabilities, we are participating in the tinyML competition entitled “tinyML Hackathon 2023: Pedestrian Detection”. Our initial results demonstrate an always-on solution (24/7) with a latency of less than 4 ms that only consumes 3 W-hr total for a whole year, that’s the equivalent of a single AA battery.
Because the OCULI SPU is fully programmable, the solution can be dynamically optimized between latency and power consumption. It will enable the first truly wireless battery-operated always-on vision products in the market. This presentation will provide an overview of Oculi’s novel vision architecture for edge applications. It will also include key results for latency and energy results for multiple use cases of interest to the TinyML community such as presence/people/pedestrian/object, face, hand, and eye detection. The results will also include a comparison with alternate or conventional solutions.

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