Abstract: Disclosed herein are systems and methods for executing a neural network (NN) across multiple processing cores. In an example embodiment, a system includes processing circuitry comprising a first processing core and a second processing core, such that the second processing core is coupled to the first processing core. Prior to executing a current layer of the NN, the second processing core determines a synchronization status of the first processing core with respect to a previous layer of the NN. Next, the second processing core executes the current layer of the NN based on data computed by the first and second processing cores with respect to the previous layer of the NN. Upon executing the current layer of the NN, the second processing core updates the first processing core with a synchronization status of the second processing core with respect to the current layer of the NN.

Abstract: A computer-implemented method automates generation of a representative volume elements (RVE) unit fuel cell model. A finite element model (FEM) of a unit cell of a proton exchange membrane fuel cell (PEMFC) is received. Input identifying a unit region with a discretization of the FE unit cell is received. A mesh rule corresponding to the unit region is received. An RVE unit region corresponding to the FE unit region is generated based on the FE unit region and the mesh rule.

Abstract: Various examples disclosed herein relate to neural network quantization techniques, and more particularly, to selecting inference precisions for the layers of the neural network. In an example embodiment, a method is provided herein that includes determining an accuracy improvement of a layer of a neural network implemented using a first bit precision relative to using a second bit precision and determining a latency degradation of the layer of the neural network implemented using the first bit precision relative to using the second bit precision. The method further includes selecting, based on the accuracy improvement and the latency degradation, the first bit precision or the second bit precision for use in implementing the layer of the neural network.

Abstract: A method for diagnosing the presence of a G147D polymorphism of human phosphatase-1 inhibitor-1 protein is provided, wherein the method involves determining the presence of the polymorphism in a biological sample from a human subject by means which detect the presence of the polymorphism, wherein the presence of the polymorphism is indicative of a predisposition to impaired functioning of the ?-adrenergic signaling pathway.