CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks

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• Alexandros Kouris (Imperial College)
• Monday 18 June 2018, 16:00-17:00
• Level 9 Seminar Room, EEE Dept..

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

In this talk I will present CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference, by exploiting the computation time-accuracy trade-off. A two-stage architecture tailored for any given FPGA device is generated, consisting of a low- and high-precision unit in a cascade. A confidence evaluation unit is employed between them to identify misclassified cases from the excessively low-precision unit and forward them to the high-precision unit for re-processing. Experiments demonstrate that the proposed toolflow can achieve a performance boost of up to 55% for VGG -16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy, without the need of retraining the model or accessing the training data.

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 Seminar Room, EEE Dept.

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