CNN-to-FPGA Toolflows: An Overview and Future Directions

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• Stelios I. Venieris (Imperial College London)
• Monday 15 January 2018, 16:00-17:00
• Level 9 Seminar Room, EE Dept..

If you have a question about this talk, please contact George A Constantinides.

Since 2012, the Deep Learning model of Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and deployment of CNN models by the Deep Learning community, several software frameworks have been developed, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPG As constitutes a promising alternative platform that can be integrated in the existing CNN ecosystem to provide a tunable balance between performance, power consumption and programmability. In this talk, an overview of the existing CNN -to-FPGA toolflows will be presented, with a focus on their key characteristics, their different strategies and design choices, together with a discussion over the open benchmarking challenges for the comprehensive and meaningful comparison between toolflows. Looking at the future of CNN -to-FPGA toolflows, the latest trends in CNN algorithmic research introduce a new set of challenges and objectives which will also be covered in this talk to point to potential research directions for the FPGA community.

This talk is part of the CAS Talks series.

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

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