Implementing an Optimized CNN Traffic Sign Recognition Solution

This is a talk from a prior Summit.
The current Summit schedule can be found here: Schedule 2018
Monday, May 1, 3:45 PM - 4:15 PM
Summit Track: 
Technical Insights I

Now that the benefits of using deep neural networks for image classification are well known, the challenge has shifted to applying these powerful techniques to build practical, cost effective solutions for commercial applications. In this presentation we’ll explain how we designed, implemented and deployed a real-world embedded traffic sign recognition solution on a heterogeneous processor.

We’ll show how we used the TensorFlow framework to train and optimize an efficient neural net classifier within the constraints of a typical embedded processor, and how we designed a modular vision pipeline to support that classifier. We’ll explain how we distributed the whole vision pipeline across the compute elements of the embedded SoC, present our methods for evaluating and optimizing each stage, and summarize the tradeoffs made in the final implementation.

We’ll also touch on solutions to some of the practical challenges of deploying CNN-based vision products, including support for multiple concurrent networks on a single device and efficiently managing remote network model updates.

Join us May 21-24, 2018 in Santa Clara, California.
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