Webinar: How to Optimize Latency for Edge AI Deployments

Learn how to optimize your deep learning models for maximum speed and efficiency.

Ran Zilberstein, VP Engineering at Deci, shares practical tips and best practices to help you leverage the full potential your edge devices covering topics such as:

  • Hardware selection: How to select the optimal hardware for your application
  • Quantization: How to reduce the precision of your neural network weights and activations to speed up inference while maintaining accuracy
  • TensorRT: How to use this NVIDIA library for optimizing deep learning models to achieve faster inference times
  • Batch size tuning: How to optimize the batch size for your model to improve inference performance
  • Multi-stream inference: How to process multiple input streams simultaneously on your device.
  • Asynchronous inference: How to maximize hardware utilization and performance with concurrent inference
  • Neural architecture search: How to accelerate inference with NAS

Through real-world examples and practical demonstrations, we’ll show you how to implement such techniques in your own machine learning projects to achieve faster processing speeds and unlock new possibilities.

If you want to learn more about optimizing your edge AI applications, book a demo here.

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")