Computing in the Deep

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• Stelios Venieris
• Monday 01 June 2015, 11:30-12:30
• Level 9 Mahanakorn Lab., EE Dept..

If you have a question about this talk, please contact Grigorios Mingas.

At the beginning of the 21st century, numerous interconnected electronic devices, such as smartphones and smart watches, are contributing to an exponential increase in the amount of data generated every day by their sensors. This data together with the Internet traffic helped to form the concept of Big Data. While data analytics algorithms aim at finding patterns and motifs, and ultimately usefulness, in such large-scale unstructured data, the emerging branch of Deep Learning seems to be one of the most promising directions. With its state-of-the-art performance in several pattern recognition tasks, such as object and speech recognition, Deep Learning can only be limited by the underlying computing infrastructure, which may not be able to handle big Deep Learning models. In this context, this talk will focus on presenting the current situation in terms of Big Data, Deep Learning and computing infrastructure, the current trends in terms of real-life Deep Learning systems with emphasis on vision applications, the computer engineering challenges that we may face and the potential role of FPGA technology in this framework.

This talk is part of the CAS Talks series.

This talk is included in these lists:

• Andrea Picciau's list
• CAS Talks
• Circuits and Systems Group: Internal Seminars
• Level 9 Mahanakorn Lab., EE Dept.

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