Paul Brasnett, Principal Research Engineer at Imagination Technologies, presents the "Training CNNs for Efficient Inference" tutorial at the May 2017 Embedded Vision Summit.

Key challenges to the successful deployment of CNNs in embedded markets are in addressing the compute, bandwidth and power requirements. Typically, for mobile devices, the problem lies in the inference, since the training is currently handled offline. One approach to reducing the inference cost is to take a trained network and use a tool to map it to a lower cost representation by, for example, reducing the precision of the weights. Better inference performance can be obtained if the cost reduction is integrated into the network training process. In this talk, Brasnett explores some of the techniques and processes that can be used during training to optimize the CNN inference performance, along with a case study to illustrate the advantages of such an approach.


May 18 - 21, Santa Clara, California

The preeminent event for practical, deployable computer vision and visual AI, for product creators who want to bring visual intelligence to products.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



1646 North California Blvd.,
Suite 360
Walnut Creek, CA 94596 USA

Phone: +1 (925) 954-1411
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