Name
Title | ||
|---|---|---|
Cascade^CNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks |
Abstract | ||
|---|---|---|
This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low-and high-precision unit in a cascade. A confidence evaluation unit is employed 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 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FPL.2018.00034 | 2018 28th International Conference on Field Programmable Logic and Applications (FPL) |
Keywords | DocType | Volume |
CNN,Convolutional Neural Networks,Quantisation,Low precision,CascadeCNN,ImageNet,VGG 16,AlexNet,FPGA based Accelerator | Conference | abs/1807.05053 |
ISSN | ISBN | Citations |
1946-147X | 978-1-5386-8518-1 | 0 |
PageRank | References | Authors |
0.34 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandros Kouris | 1 | 22 | 2.83 |
| Stylianos I. Venieris | 2 | 106 | 12.98 |
| Christos-Savvas Bouganis | 3 | 37 | 7.60 |