Automatic prediction of the execution time of programs for a given architecture is crucial, both for performance analysis in general and for compiler designers in particular. In this paper, we present CATREEN, a recurrent neural network able to predict the steady-state execution time of each basic block in a program. Contrarily to other models, CATREEN can take into account the execution context formed by the previously executed basic blocks which allows accounting for the processor micro-architecture without explicit modeling of micro-architectural elements (caches, pipelines, branch predictors, etc.). The evaluations conducted with synthetic programs and real ones (Programs from Mibench and Polybench) show that CATREEN can provide accurat...
Deep neural networks have revolutionized multiple fields within computer science. It is important to...
Recurrent Neural Networks (RNNs) have shown great success in sequence-to-sequence processing due to ...
This thesis proposes a convolutional long short-term memory neural network model for predicting limi...
Automatic prediction of the execution time of programs for a given architecture is crucial, both for...
International audienceAutomatic prediction of the execution time of programs for a given architectur...
Abstract. We present an estimation methodology, accurately predicting the execution time for a given...
© 2019 by the author(s). Predicting the number of clock cycles a processor takes to execute a block ...
Predicting the completion time of business process instances would be a very helpful aid when managi...
Embedded systems need to respect stringent real time constraints. Various hardware components includ...
Abstract machines provide a certain separation between platform-dependent and platform-independent ...
Abstract In this work we use Machine Learning (ML) tech-niques to learn the CPU time-slice utilizat...
International audienceLong pipelines need good branch predictors to keep the pipeline running. Curre...
Predicting the next activity of a running execution trace of a business process represents a challen...
Storing instructions in caches has led to dramatic increases in the speed at which programs can exec...
Sequence prediction and classification are ubiqui-tous and challenging problems in machine learn-ing...
Deep neural networks have revolutionized multiple fields within computer science. It is important to...
Recurrent Neural Networks (RNNs) have shown great success in sequence-to-sequence processing due to ...
This thesis proposes a convolutional long short-term memory neural network model for predicting limi...
Automatic prediction of the execution time of programs for a given architecture is crucial, both for...
International audienceAutomatic prediction of the execution time of programs for a given architectur...
Abstract. We present an estimation methodology, accurately predicting the execution time for a given...
© 2019 by the author(s). Predicting the number of clock cycles a processor takes to execute a block ...
Predicting the completion time of business process instances would be a very helpful aid when managi...
Embedded systems need to respect stringent real time constraints. Various hardware components includ...
Abstract machines provide a certain separation between platform-dependent and platform-independent ...
Abstract In this work we use Machine Learning (ML) tech-niques to learn the CPU time-slice utilizat...
International audienceLong pipelines need good branch predictors to keep the pipeline running. Curre...
Predicting the next activity of a running execution trace of a business process represents a challen...
Storing instructions in caches has led to dramatic increases in the speed at which programs can exec...
Sequence prediction and classification are ubiqui-tous and challenging problems in machine learn-ing...
Deep neural networks have revolutionized multiple fields within computer science. It is important to...
Recurrent Neural Networks (RNNs) have shown great success in sequence-to-sequence processing due to ...
This thesis proposes a convolutional long short-term memory neural network model for predicting limi...