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This work presents CascadeCNN, an automated toolflow that pushes thequantisation limits of any given CNN model, aiming to perform high-throughputinference. A two-stage architecture tailored for any given CNN-FPGA pair isgenerated, consisting of a low- and high-precision unit in a cascade. Aconfidence evaluation unit is employed to identify misclassified cases from theexcessively low-precision unit and forward them to the high-precision unit forre-processing. Experiments demonstrate that the proposed toolflow can achieve aperformance boost up to 55% for VGG-16 and 48% for AlexNet over the baselinedesign for the same resource budget and accuracy, without the need ofretraining the model or accessing the training data.

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