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IJCAI is the International Joint Conference on Artificial Intelligence, the main international gathering of researchers in AI. The 26th International Joint Conference on Artificial Intelligence will be held in Melbourne, Australia in August 2017.
International Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video recordings, and other educational materials. IJCAI conferences present premier international gatherings of AI researchers and practitioners. IJCAI conferences were held biennially in odd-numbered years since 1969. They are sponsored jointly by International Joint Conferences on Artificial Intelligence Organization(IJCAI), and the national AI societie(s) of the host nation(s).
Neural Network Exchange Format for the Deployment of Trained Networks to Inference Engines
Talk by: Viktor Gyenes, AIMotive
Date &Time: August 19, all day
Location: Building 80, Floor 7, Room 2
Neural networks are successfully being used to solve difficult tasks in image, audio and text processing. Several deep learning frameworks are available for the research community to train such networks. Neural networks require massive amounts of computational power, therefore chip vendors are working on new hardware solutions to accelerate computations, with accompanying libraries to drive their hardware. Low-power embedded hardware solutions are needed in industrial segments, such as autonomous driving, that heavily utilize pattern recognition. Network inference libraries need to be able to digest the products of various deep learning frameworks in order to deploy networks to devices across multiple platforms, and the market is in danger of fragmenting. The Khronos Group, an open consortium of leading hardware and software companies has authored various industrial compute standards, and is now working on developing a Neural Network Exchange Format to facilitate the deployment of trained neural networks from deep learning frameworks to hardware accelerated inference engines. The goal of the exchange format is to describe neural network structure and data in a unified way, with standardized semantics, that can be exported from deep learning frameworks, and is easy to digest for inference engines. The talk will overview the standardization activities, describe the general design principles of the planned exchange format, along with use cases and expected industry outcomes.
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