Duration: 50 mins. 20 mins of slides, 20 mins live demo, and 10 mins Q&A.

Content: We briefly present and demo how to apply the following TensorFlow based optimizers on CNNs

1. Pre-training Optimization: Quantization-aware training, Pruning.
2. Post-training Optimization: Int with float fallback, Float16, Integer-only quantization.
3. Operations optimization.
4. Graph Optimization.

Then we demo joint optimization by combining more than one of the above optimizers. Based on experiment result analysis, we present the best optimization sequence for smallest model size, accuracy preservation, and fast Inference.

Outcome: The audience can apply the learned optimization techniques on the models from a growing number of use-cases such as anomaly detection, predictive maintenance, robotics, voice recognition, machine vision, etc., to enable standalone device-level execution. Thus, we believe this part of the tutorial session opens future avenues for a broad-spectrum of applied research works.