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"Collaboratively Benchmarking and Optimizing Deep Learning Implementations," a Presentation from General Motors

Unmesh Bordoloi, Senior Researcher at General Motors, presents the "Collaboratively Benchmarking and Optimizing Deep Learning Implementations" tutorial at the May 2017 Embedded Vision Summit.

For car manufacturers and other OEMs, selecting the right processors to run deep learning inference for embedded vision applications is a critical but daunting task.  One challenge is the vast number of options in terms of neural network models, frameworks (such as Caffe, TensorFlow, Torch), and libraries such as CUDA and OpenCL. Another challenge is the large number of network parameters that can affect the computation requirements, such the choice of training data sets, precision, and batch size.  These challenges also complicate efforts to optimize implementations of deep learning algorithms for deployment.

In this talk, Bordoloi presents a methodology and open-source software framework for collaborative and reproducible benchmarking and optimization of convolutional neural networks.  General Motors' software framework, CK-Caffe, is based on the Collective Knowledge framework and the Caffe framework.  GM invites the community to collaboratively evaluate, design and optimize convolutional neural networks to meet the performance, accuracy and cost requirements of a variety of applications – from sensors to self-driving cars.