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Towards an Automated R&D Workflow for Edge AI Systems

Posted Sep 23
# ML Workflow
# Edge AI Systems
# SightX AI
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SPEAKER
Dan Malowany
Dan Malowany
Dan Malowany
VP R&D @ SightX AI

Dan Malowany is currently the VP R&D at SightX AI, an edge AI company, focused on computer vision-based autonomy, for SWaP-sensitive platforms. SightX provides data-driven computer vision capabilities that run, in real-time, on light SOMs (System On Module). Its deep learning algorithms are operating at the edge, providing real-time situational awareness.

Previous to his position at SightX AI, Dan was involved in numerous R&D roles, including head of research at the MLOPS company Allegro AI and as the head of the UGS (Unattended Ground Sensors) section at the Directorate for Defense Research & Development (DDR&D). During his work at DDR&D, he had a significant role in managing various research and development programs for the homeland security sector.

Dan Malowany received his Ph.D. degree in Electrical and Computer Engineering in 2018 from the Ben-Gurion University of the Negev in Israel. His Ph.D. research at the Laboratory of Autonomous Robotics (LAR) was focused on analyzing and designing an architecture that integrates mechanisms of the human visual system with state-of-the-art deep convolutional neural networks.

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Dan Malowany is currently the VP R&D at SightX AI, an edge AI company, focused on computer vision-based autonomy, for SWaP-sensitive platforms. SightX provides data-driven computer vision capabilities that run, in real-time, on light SOMs (System On Module). Its deep learning algorithms are operating at the edge, providing real-time situational awareness.

Previous to his position at SightX AI, Dan was involved in numerous R&D roles, including head of research at the MLOPS company Allegro AI and as the head of the UGS (Unattended Ground Sensors) section at the Directorate for Defense Research & Development (DDR&D). During his work at DDR&D, he had a significant role in managing various research and development programs for the homeland security sector.

Dan Malowany received his Ph.D. degree in Electrical and Computer Engineering in 2018 from the Ben-Gurion University of the Negev in Israel. His Ph.D. research at the Laboratory of Autonomous Robotics (LAR) was focused on analyzing and designing an architecture that integrates mechanisms of the human visual system with state-of-the-art deep convolutional neural networks.

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SUMMARY

The R&D workflow of an AI-based product is inherently characterized by the experimental nature of the deep-tech research process. Adding to the challenges of edge technology - working on various ARM-based SOMs with multiple GPUs and DSP types, the inevitable conclusion is that a bespoke R&D methodology is required.

This talk discusses SightX AI's design and successful application of an end-to-end MLOPS methodology. The proposed design enabled us to tackle the management of deep learning research aimed to be deployed on various platforms and to become faster and better with every version release. SightX AI recently added a feedback loop to this methodology which gets us a step closer to the holy grail of automated and continuously learning R&D workflow for edge AI.

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