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QP real-time event frameworks (RTEFs) provide lightweight, modern, event-driven architecture based on asynchronous Active Objects (Actors) and Hierarchical State Machines. The matching QM model-based design tool and other host-based tools complement the QP frameworks by supporting graphical modeling, code generation, software tracing, and unit testing for event-driven embedded software. Visit https://www.state-machine.com for more information. The QP RTEFs can run on bare-metal...

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. ...