Abstract: A device for obtaining a local optimal AI model may include an artificial intelligence (AI) chip and a processing device configured to receive a first initial AI model from the host device. The device may load the initial AI model into the AI chip to determine a performance value of the AI model based on a dataset, and determine a probability that a current AI model should be replaced by the initial AI model. The device may determine, based on the probability, whether to replace the current AI model with the initial AI model. If it is determined that the current AI model be replaced, the device may replace the current AI model with the initial AI model. The device may repeat the above processes and obtain a final current AI model. The device may transmit the final current AI model to the host device.

Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

Abstract: A system may include a decentralized communication network and multiple processing devices on the network. Each processing device may have an artificial intelligence (AI) chip, the device may be configured to generate an AI model, determine the performance value of the AI model on the AI chip, receive a chain from the network where the chain contains a performance measure. If the performance value of the AI model is better than the performance measure, then the processing device may broadcast the AI model to the network for verification. If the AI model is verified by the network, the device may update the chain with the performance value so that the chain can be shared by the multiple processing devices on the network. Any processing device on the network may also verify an AI model broadcasted by any other device. Methods for generating the AI model are also provided.

Abstract: Methods and systems for extracting features directly from convolutional layers are disclosed. The last layer in the ordered convolutional layers contains reduced number of channels of features with respect to the immediately prior layer. Filter coefficients of the convolutional layers are trained for image classification task together with fully-connected networks. For image verification task, filter coefficients can be trained using Siamese networks. Training of the filter coefficients is performed in the sequential order of ordered convolutional layers. Once trained, the ordered convolutional layers with the last layer having reduced number of channels can be used directly for extracting features with acceptable accuracy in certain applications (e.g., face verification). Trained filter coefficients can optionally be converted to bi-valued filter coefficients, and then be loaded into a cellular neural networks (CNN) based digital integrated circuit.

Abstract: A computer implemented method for dynamic data rate adjustment within a cascaded forward error correction FEC for optical communications includes subjecting data communicated over an optical network to a forward error correction in an encoding or decoding of the data, the encoding or decoding employing a codeword, re-encoding part of the codeword for generating a subsequent codeword where an actual code rate is tuned by adjusting a size of data encoded to provide re-encoded data, and dynamically changing the re-encoded data size to achieve cascaded rate adaptive FEC for communication of the data over the optical network.

Abstract: A computer implemented method for a cyclic (forward-backward) decoding for a forward error-correction FEC scheme includes decoding a given k?1th codeword in a block code of length N in an optical communication system, forwarding M symbols' enhanced log likelihood ratios LLRs produced by decoding the k?1th codeword, decoding the kth codeword together with forwarded M symbols' enhanced LLRS, and feeding backward, to the initial step i) decoding, corresponding overlapped M symbols' enhanced LLRs for decoding of the k?1th codeword again.

Abstract: Systems and method relating generally to data processing, and more particularly to systems and methods for decoding information. Some disclosed systems include a first data decoding circuit, a second data decoding circuit, and a data output circuit. The second data decoding circuit is coupled to the first data decoding circuit and the data output circuit. The second data decoding circuit is operable to apply a finite alphabet iterative decoding algorithm to the first decoded output to yield a second decoded output.

Abstract: A computer implemented method for dynamic data rate adjustment within a cascaded forward error correction FEC for optical communications includes subjecting data communicated over an optical network to a forward error correction in an encoding or decoding of the data, the encoding or decoding employing a codeword, re-encoding part of the codeword for generating a subsequent codeword where an actual code rate is tuned by adjusting a size of data encoded to provide re-encoded data, and dynamically changing the re-encoded data size to achieve cascaded rate adaptive FEC for communication of the data over the optical network.

Abstract: Systems and method relating generally to data processing, and more particularly to systems and methods for decoding information.

Abstract: A computer implemented method for a cyclic (forward-backward) decoding for a forward error-correction FEC scheme includes decoding a given k?1th codeword in in a block code of length N in an optical communication system, forwarding M symbols' enhanced log likelihood ratios LLRs produced by decoding the k?1th codeword, decoding the kth codeword together with forwarded M symbols' enhanced LLRS, and feeding backward, to the initial step i) decoding, corresponding overlapped M symbols' enhanced LLRs for decoding of the k?1 th codeword again.