Abstract: A system may include multiple client devices and a processing device communicatively coupled to the client devices. One or more client devices may implement a greedy approach in searching for an optimal artificial intelligence (AI) model. For example, a client device may use a training dataset to perform an AI task, and update its AI model. The client device may verify the performance of the AI task and determine whether to accept or reject its updated AI model. Upon rejection, the client device may repeat updating its AI model until the updated AI model is accepted, or until a stopping criteria is met. The processing device may be configured to update the initial AI models based on the accepted updated AI models obtained in the multiple client device. Training data for each of the client devices may contain a subset shuffled from a larger training dataset.

Abstract: A cellular neural network architecture may include a processor and an embedded cellular neural network (CeNN) executable in an artificial intelligence (AI) integrated circuit and configured to perform certain AI functions. The CeNN may include multiple convolution layers, such as first, second, and third layers, each layer having multiple binary weights. In some examples, a method may configure the multiple layers in the CeNN to produce a residual connection. In configuring the second and third layers, the method may use an identity matrix.

Abstract: A cellular neural network architecture may include a processor and embedded cellular, neural network (CeNN) executable in an artificial intelligence (AI) integrated circuit and configured to perform certain AI functions. The CeNN may include multiple convolution layers, each having multiple binary weights. In some examples, a method may configure a given layer of the CeNN and one or more additional layers of the CeNN to retrieve the output of the given layer for debugging or training the CeNN. In configuring the one or more additional layers, the method may use an identity layer.

Abstract: A system may include multiple client devices and a processing device communicatively coupled to the client devices. One or more client devices may implement a greedy approach in searching for an optimal artificial intelligence (AI) model. For example, a client device may use a training dataset to perform an AI task, and update its AI model. The client device may verify the performance of the AI task and determine whether to accept or reject its updated AI model. Upon rejection, the client device may repeat updating its AI model until the updated AI model is accepted, or until a stopping criteria is met. The processing device may be configured to update the initial AI models based on the accepted updated AI models obtained in the multiple client device. Training data for each of the client devices may contain a subset shuffled from a larger training dataset.

Abstract: A system may include multiple client devices and a processing device communicatively coupled to the client devices. A client device may receive an initial artificial intelligence (AI) model, use a training dataset to perform an AI task, and update its AI model. The client device may verify the performance of the AI task to determine whether to accept or reject its updated AI model. Upon rejection, the client device may repeat updating its AI model until the updated AI model is accepted, or until a stopping criteria is met. The processing device may be configured to update the initial AI models based on the accepted updated AI models obtained in the multiple client devices, and repeat the process for each client device using the updated initial AI models. Training data for each of the client devices may contain a subset shuffled from a larger training dataset.

Abstract: An automotive ceramic friction material free from asbestos and metal and preparation method thereof are provided. The material includes the following components: organic adhesive, reinforced fiber, friction-increasing agent, antifriction agent and fillers. The material has high coefficient of friction, stable braking performance, low heat fading, low wear resistance and long service life.