Abstract: Disclosed are an in-memory computing architecture for a nearest neighbor search of a cosine distance and an operating method thereof. The in-memory computing architecture comprises two FeFET-based storage arrays, Translinear circuits and a WTA circuit, and the two storage arrays are a first storage array and a second storage array, respectively; wherein each of the storage cells comprises a FeFET and a resistor which are electrically connected; an input vector is inputted into the first storage array for outputting the inner product X of the input vector multiplied by all the storage vectors in the first storage array; the second storage array outputs the sum of squares Y of all vector elements in the storage vectors; the output values of the first storage array and the second storage array are respectively inputted into the Translinear circuits through current mirrors; and the Translinear circuits output X2/Y to the WTA circuit.

Abstract: The present application provides a displaying base plate and a displaying device, which relates to the technical field of displaying. The displaying device can ameliorate the problem of screen greening caused by electrostatic charges, thereby improving the effect of displaying. The displaying base plate includes an active area and a non-active area connected to the active area, the non-active area includes an edge region and a first-dam region, and the first-dam region is located between the active area and the edge region; the displaying base plate further includes: a substrate; an anti-static layer disposed on the substrate, wherein the anti-static layer is located at least within the edge region; and a driving unit and a touch unit that are disposed on the substrate, wherein the driving unit is located within the active area.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: The invention discloses a neural network retraining and gradient sparse method based on the aging sensing of memristors. For the accuracy rate of hardware online reasoning decreases after cross-array aging, the extreme values of programmable weights under a current aging condition is calculated by using the known aging information of memristor, and a neural network model is retrained according to this, so as to improve the accuracy rate of the current hardware online reasoning. In the process of retraining, network weights exceeding the extreme values of programmable weights are automatically truncated. For the working life of the memristor, the sparsity of derivatives of the neural network is utilized to discard the derivatives with a small absolute value in hardware adjustment process, so as to ensure that the memristor corresponding to small derivatives would not be applied voltage, prevent the aging process of the memristors and prolong the service life thereof.

Abstract: Disclosed in the present invention is an ultra-compact CAM array based on a single MTJ and an operating method thereof. The CAM array comprises an M*N CAM core for storing contents, additional reference rows for storing “0” and “1” and reference columns for storing “0” and “1”, a row decoder, a column decoder, transmission gates ENs, write drivers WDs, search current sources Isearchs and two-stage detection amplifiers. The present invention utilizes 1T-1MTJ cells to construct the CAM array, and combines the advantages of the MTJ and CMOS. While ensuring search energy efficiency, a unique structure of the MTJ is utilized to implement a less area overhead and a lower search delay compared with a traditional CMOS-based CAM, and non-volatility is achieved.

Abstract: A latency adjustment method includes the following operations: in response to a predetermined event of a data stream occurred during a first interval, performing a transmission status determining operation to determine whether a transmission status of the data stream is stable; in response the transmission status being stable, determining whether a total number of times of packet loss compensation events of the data stream occurred during a previous interval is higher than a first predetermined value; and in response to the transmission status being unstable or the total number of times being higher than the first predetermined value, increasing a latency of the data stream.

Abstract: The present invention discloses an energy-efficient capacitance extraction method based on machine learning, and involves improving parameter extraction efficiency by using a machine learning model to extract parasitic capacitance; generally representing an interconnection line structure by grid-based data representation; reducing a workload of parameter extraction and enhancing the robustness of different semiconductor technologies with the idea of an adaptive extraction window; establishing a machine learning model of capacitance extraction for a two-dimensional interconnection line structure, and extracting grid parameters of a target interconnection line structure and inputting the grid parameters into the machine learning model, thereby obtaining parasitic capacitance parameters. Compared with an existing capacitance extraction technology, an capacitance extractor has achieved excellent performance in accuracy, speed and time and space consumption.

Abstract: Disclosed in the present invention are a highly energy-efficient CAM based on a single FeFET and an operating method thereof, which relate to a design of an FeFET-based memory suitable for low power consumption and high performance. A brand-new design of a CAM cell based on the single FeFET is achieved by fully utilizing the storage characteristics of the FeFET, so that the number of transistors is saved, the search energy consumption is reduced, and the nonvolatility of data storage is obtained. The present invention utilizes a 2T-1FeFET structure, and combines the advantages of the FeFET and CMOS. Without reducing performance, only one FeFET is utilized to implement a less area overhead and a lower energy consumption compared with a traditional CMOS-based CAM, and non-volatility is achieved.

Abstract: The present disclosure discloses a three-dimensional automatic location system for an epileptogenic focus based on deep learning. The system includes: a PET image acquisition and labelling module; a registration module mapping PET image to standard symmetrical brain template; a PET image preprocessing module generating mirror image pairs of left and right brain image blocks; a network SiameseNet training module containing two deep residual convolutional neural networks which share weight parameters, an output layer connecting a multilayer perceptron and a softmax layer, and using a training set of an epileptogenic focus image and an normal image to train the network to obtain a network model; a classification module and epileptogenic focus location module, using the trained network model to generate a probabilistic heatmap for the newly input PET image, a classifier determining whether the image is normal or epileptogenic focus sample, and then predicting a position for the epileptogenic focus region.

Abstract: A latency adjustment method includes the following operations: in response to a predetermined event of a data stream occurred during a first interval, performing a transmission status determining operation to determine whether a transmission status of the data stream is stable; in response the transmission status being stable, determining whether a total number of times of packet loss compensation events of the data stream occurred during a previous interval is higher than a first predetermined value; and in response to the transmission status being unstable or the total number of times being higher than the first predetermined value, increasing a latency of the data stream.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: The present invention discloses a neural network training method for a memristor memory for memristor errors, which is mainly used for solving the problem of decrease in inference accuracy of a neural network based on the memristor memory due to a process error and a dynamic error. The method comprises the following steps: performing modeling on a conductance value of a memristor under the influence of the process error and the dynamic error, and performing conversion to obtain a distribution of corresponding neural network weights; constructing a prior distribution of the weights by using the weight distribution obtained after modeling, and performing Bayesian neural network training based on variational inference to obtain a variational posterior distribution of the weights; and converting a mean value of the variational posterior of the weights into a target conductance value of the memristor memory.

Abstract: The present invention discloses an error unbiased approximate multiplier for normalized floating-point numbers and an implementation method of the error unbiased approximate multiplier. The error unbiased approximate multiplier includes a symbol and exponent bit module, a mantissa approximation module and a normalization module, wherein the symbol and exponent bit module processes symbolic operation and exponent bit operation of the floating-point numbers; the mantissa approximation module obtains a mantissa approximation result under different accuracy requirements by summing a result of multilevel error correction modules; and the normalization module adjusts an exponent bit according to the operation result of the mantissa and processes the overflow of the exponent bit to obtain the final product result.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.

Abstract: The present disclosure discloses a three-dimensional automatic location system for an epileptogenic focus based on deep learning. The system includes: a PET image acquisition and labelling module; a registration module mapping PET image to standard symmetrical brain template; a PET image preprocessing module generating mirror image pairs of left and right brain image blocks; a network SiameseNet training module containing two deep residual convolutional neural networks which share weight parameters, an output layer connecting a multilayer perceptron and a softmax layer, and using a training set of an epileptogenic focus image and an normal image to train the network to obtain a network model; a classification module and epileptogenic focus location module, using the trained network model to generate a probabilistic heatmap for the newly input PET image, a classifier determining whether the image is normal or epileptogenic focus sample, and then predicting a position for the epileptogenic focus region.

Abstract: Disclosed in the present invention is a mixed-granularity-based joint sparse method for a neural network. The joint sparse method comprises independent vector-wise fine-grained sparsity and block-wise coarse-grained sparsity; and a final pruning mask is obtained by performing a bitwise logic AND operation on pruning masks independently generated by two sparse methods, and then a weight matrix of the neural network after sparsity is obtained. The joint sparsity of the present invention always obtains the reasoning speed between a block sparsity mode and a balanced sparsity mode without considering the vector row size of the vector-wise fine-grained sparsity and the vector block size of the block-wise coarse-grained sparsity. Pruning for a convolutional layer and a fully-connected layer of a neural network has the advantages of variable sparse granularity, acceleration of general hardware reasoning and high accuracy of model reasoning.

Abstract: The present invention discloses an error unbiased approximate multiplier for normalized floating-point numbers and an implementation method of the error unbiased approximate multiplier. The error unbiased approximate multiplier includes a symbol and exponent bit module, a mantissa approximation module and a normalization module, wherein the symbol and exponent bit module processes symbolic operation and exponent bit operation of the floating-point numbers; the mantissa approximation module obtains a mantissa approximation result under different accuracy requirements by summing a result of multilevel error correction modules; and the normalization module adjusts an exponent bit according to the operation result of the mantissa and processes the overflow of the exponent bit to obtain the final product result.

Abstract: A convolutional neural network pruning method based on feature map sparsification, which relates to how to compress the convolutional neural network to reduce the number of parameters and amount of computation so as to facilitate actual deployment, is provided. In the training process, by adding regularization to the feature map L1 or L2 after the activation layer in the loss function, the corresponding feature map channels have different sparsity. Under a certain pruned ratio, the convolution kernels corresponding to the channels are pruned according to the sparsity of the feature map channels. After fine-tune pruning, the network obtains new accuracy, and the pruned ratio is adjusted according to the change of accuracy before and after pruning. After multiple iterations, the near-optimal pruned ratio is found, and pruning is realized to the maximum extent under the condition that the accuracy does not decrease.

Abstract: The disclosure provides a transmitter of a communication system using hybrid digital/analog beamforming and configured to perform digital pre-distortion (DPD). In an exemplary embodiment in accordance with the disclosure, the transmitter may generate a plurality of scrambling sequences. The transmitter may comprise a plurality of combining modules to receive a combined feedback signal. The transmitter may use the plurality of scrambling sequences to recover the signals output by the antenna arrays from the combined feedback signal. Thus, the transmitter may perform DPD for each antenna array.

Abstract: A processing method that is performed by one or more processor is provided. The processing method includes determining a target video frame in a currently captured video; determining an object area in the target video frame based on a box selection model; determining a category of a target object in the object area based on a classification model used to classify an object in the object area; obtaining augmented reality scene information associated with the category of the target object; and performing augmented reality processing on the object area in the target video frame and the augmented reality scene information, to obtain the augmented reality scene.