Abstract: A computer-implemented method for dynamically executing an application program by a platform including a processor with a program memory and a programmable logic unit, wherein a first application program having a first module is loaded from an application database into the program memory and/or the programmable logic unit during a programming mode and executed as first program code during an execution mode, where a processor or the programmable logic unit performs a check during the execution mode based on a predefined criterion to determine whether a second application program having a second module should be loaded from the application database and, if so, the system switches to the programming mode and the second module is loaded into the program memory and/or the programmable logic unit and executed as second program code, where the system switches to the execution mode in which the second program code is executed.

Abstract: Provided is multi-layer neural network technique that includes: calculating, from an input and using a first one or more layers of a plurality of layers of a neural network, a first intermediate output; reducing a size of one or more dimensions of the first intermediate output; calculating, from the first intermediate output and using a second one or more layers of the neural network, a second intermediate output (the second one or more layers including one or more ultra-low precision layers); reducing a size of one or more dimensions of the second intermediate output; combining a plurality of reduced intermediate outputs (including the reduced first intermediate output and the reduced second intermediate output) to derive a combined intermediate output; and calculating, using the combined intermediate output and one or more higher-precision layers of the plurality of layers, a neural network output.

Abstract: A computer-implemented method comprising: obtaining an input; processing the input using a neural network comprising a plurality of layers, comprising: calculating, using a first one or more layers of the plurality of layers, a first intermediate output from the input; reducing a size of one or more dimensions of the first intermediate output; calculating, using a second one or more layers, a second intermediate output from the first intermediate output, the second one or more layers comprising one or more ultra-low precision layers; reducing a size of one or more dimensions of the second intermediate output: combining a plurality of reduced intermediate outputs to derive a combined intermediate output, wherein the plurality of reduced intermediate outputs comprise the reduced first intermediate output and the reduced second intermediate output; and calculating, using one or more higher-precision layers of the plurality of layers, a neural network output using the combined intermediate output; and outputting