It’s wonderful to see when members of the big data ecosystem team up large industry players for some late-breaking research results.
Case in point, our friends over at DarwinAI recently revealed significant research results in a joint paper with German auto manufacturer Audi.
The paper is titled “Human-Machine Collaborative Design for Accelerated Design of Compact Deep Neural Networks for Autonomous Driving.
” The research focuses on how human-machine collaboration accelerates the design of compact deep neural networks for autonomous driving.
Basically, the creativity of a human designer has been combined with the raw speed of AI to create better, more compact networks tailored for specific tasks more quickly.
The research includes talent hours for generating an optimal model, GPU processing hours and also cost savings.
The following is a summary of the research results: An effective deep learning development process is critical for widespread industrial adoption, particularly in the automotive sector.
A typical industrial deep learning development cycle involves customizing and re-designing an off-the-shelf network architecture to meet the operational requirements of the target application, leading to considerable trial and error work by a machine learning practitioner.
This approach greatly impedes development with a long turnaround time and the unsatisfactory quality of the created models.
As a result, a development platform that can aid engineers in greatly accelerating the design and production of compact, optimized deep neural networks is highly desirable.
In this joint industrial case study, we study the efficacy of the GenSynth AI-assisted AI design platform for accelerating the design of custom, optimized deep neural networks for autonomous driving through human-machine collaborative design.
We perform a quantitative examination by evaluating 10 different compact deep neural networks produced by GenSynth for the purpose of object detection via a NASNet-based user network prototype design, targeted at a low-cost GPU-based accelerated embedded system.
Furthermore, we quantitatively assess the talent hours and GPU processing hours used by the GenSynth process and three other approaches based on the typical industrial development process.
In addition, we quantify the annual cloud cost savings for comprehensive testing using networks produced by GenSynth.
Finally, we assess the usability and merits of the GenSynth process through user feedback.
The findings of this case study showed that GenSynth is easy to use and can be effective at accelerating the design and production of compact, customized deep neural network.
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