Collaboratively Benchmarking and Optimizing Deep Learning Implementations
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, we present a methodology and open-source software framework for collaborative and reproducible benchmarking and optimization of convolutional neural networks. Our software framework, CK-Caffe, is based on the Collective Knowledge framework and the Caffe framework. We invite 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.
Unmesh Bordoloi is a Senior Researcher at General Motors R&D where he is working on computational platforms for autonomous driving. Prior to this he was a faculty member at Linkoping University in Sweden where he obtained a tenured position. He also spent one year as a post-doc at Verimag, France. He graduated from National University of Singapore with a PhD and from National Institute of Roukela with a B.Tech degree.