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Adjoint computation and Backpropagation

Guillaume Pallez 1
1 TADAAM - Topology-Aware System-Scale Data Management for High-Performance Computing
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest
Abstract : In this talk Dr Pallez will discuss the impact of memory in the computation of automatic differentiation or for the backpropagation step of machine learning algorithms. He will show different strategies based on the amount of memory available. In particular he will discuss optimal strategies when one can reuse memory slots, and when considering a hierarchical memory platform
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Contributor : Guillaume Pallez (aupy) Connect in order to contact the contributor
Submitted on : Monday, December 9, 2019 - 4:16:51 PM
Last modification on : Tuesday, December 8, 2020 - 9:58:04 AM
Long-term archiving on: : Tuesday, March 10, 2020 - 8:43:14 PM


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  • HAL Id : hal-02400746, version 1



Guillaume Pallez. Adjoint computation and Backpropagation. Meeting of the Royal Society -- Numerical algorithms for high-performance computational science, Apr 2019, London, United Kingdom. ⟨hal-02400746⟩



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