Conference paper Open Access

SkipW: Resource Adaptable RNN with Strict Upper Computational Limit

Mayet, Tsiry; Lambert, Anne; Le Guyadec, Pascal; Le Bolzer, Francoise; Schnitzler, Francois

We introduce Skip-Window, a method to allow recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence. Similarly to existing approaches, Skip-Window extends existing RNN cells by adding a mechanism to encourage the model to process fewer inputs. Unlike existing approaches, Skip-Window is able to respect a strict computational budget, making this model more suitable for limited hardware like edge devices. We evaluate this approach on four datasets: a human activity recognition task, sequential MNIST, IMDB and adding task. Our results show that Skip-Window is often able to exceed the accuracy of existing approaches for a lower computational cost while strictly limiting said cost.

Files (1.2 MB)
Name Size
1.2 MB Download
All versions This version
Views 1717
Downloads 1111
Data volume 12.7 MB12.7 MB
Unique views 1414
Unique downloads 1010


Cite as