Speaker
Jean-Luc Rey
(Bloomberg)
Description
Bidirectional random number generators (RNGs) allow stochastic sequences to be reproduced not only forward but also backward in time. This capability can be leveraged in adjoint automatic differentiation (AD) to significantly reduce memory usage: instead of storing all intermediate random variates on the tape for backpropagation, the AD engine can efficiently regenerate them in reverse order at negligible computational cost. We demonstrate this idea in a representative computational-finance setting, showing how bidirectional RNGs enable lighter adjoint memory footprints while preserving the accuracy and performance of gradient calculations.