CascadeCNN: Pushing the performance limits of quantisation

05/22/2018
by   Alexandros Kouris, et al.
0

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, to perform high-throughput inference by exploiting the computation time-accuracy trade-off. Without the need for retraining, a two-stage architecture tailored for any given FPGA device is generated, consisting of a low- and a high-precision unit. A confidence evaluation unit is employed between them to identify misclassified cases at run time and forward them to the high-precision unit or terminate computation. Experiments demonstrate that CascadeCNN achieves a performance boost of up to 55 the same resource budget and accuracy.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset