Human-Machine Collaborative Design for Accelerated Design of Compact Deep Neural Networks for Autonomous Driving

TL;DR

This paper demonstrates that the GenSynth AI-assisted platform significantly accelerates the design of compact, optimized deep neural networks for autonomous driving, reducing development time and costs through human-machine collaboration.

Contribution

It introduces and evaluates the GenSynth platform for collaborative neural network design, showing its effectiveness in reducing development effort and cost in industrial autonomous driving applications.

Findings

GenSynth accelerates neural network design process.

It reduces talent and GPU hours compared to traditional methods.

GenSynth offers cost savings in cloud testing.

Abstract

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.