In this paper we will give a retrospective of machine learning in compiler optimisation from its earliest inception, through some of the works that set themselves apart, to today's deep learning, finishing with our vision of the field's future.Hugh Leather...
Machine Learning in Compiler Optimisation - Zheng Wang and Michael O'Boyle, Proceedings of the IEEE, 2018 A survey on compiler autotuning using machine learning - Amir H. Ashouri, William Killian, John Cavazos, Gianluca Palermo, and Cristina Silvano, ACM Computing Surveys (CSUR), 2018 A ...
Interested in .NET&Java platform,CLR and JVM internal,C# ,JIT compiler,software architecture design,windows kernel/CLR debugging skills,SQL Server 、MySQL,Database architecture、Query Optimization、troubleshooting and high availability, parallel/multi-threaing programming,distributed computing,cloud computing ...
A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. Also, a listed repository should be deprecated if:...
cONNXr - An ONNX runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine learning models no matter which framework you train it with. Easy to install and compiles everywhere, even in very old devices. libonnx - A lightweight...
Regit a travaillé avec Peak Business Insight, un partenaire consultant APN avancé, afin de mettre en application des « modèles de machine learning basés sur des catégories » capables de gérer simultanément des données de catégorie et des données variables afin de prévoir la probabil...
Dois-je utiliser Amazon SageMaker Neo pour compiler mon modèle afin d'utiliser Amazon SageMaker Edge Manager ? Comment déployer des modèles sur les appareils de périphérie ? En quoi le kit SDK Amazon SageMaker Edge Manager diffère-t-il de l'exécution SageMaker Neo (dlr) ? Quelle est ...
We present a general technique for constructing Graph Neural Networks (GNNs) capable of using multi-relational domain knowledge. The technique is based on
Kurek, M., Deisenroth, M.P., Luk, W., Todman, T.: Knowledge transfer in automatic optimisation of reconfigurable designs. In: IEEE Symposium on Field Programmable Custom Computing Machines (FCCM) (2016) Google Scholar Kwon, J., Carloni, L.P.: Transfer learning for design-space exploratio...
Its success in such a challenge has been checked for problems in a variety of fields, such as software-hardware co-design [23,24,25], compiler optimization [26], student profiling in e-learning environments [27,28], identification of phytopathology from sensor data [29], systematic literature...