Shanghai Cambricon Information Technology Co., Ltd

Xishan Zhang

Shaoli Liu

Jiaming Guo

Xiaofu Meng

Yao Zhang

Chang Liu

Di Huang

Tianshi Chen

Yu Chen

Shuai Hu

Xinkai Song

Shengyuan Zhou

Yuzhe Luo

Guang Jiang

Shiyi Zhou

A neural network operation module, which comprises a storage unit that stores output neurons, weight precision and output neuron gradient precision of a multi-layer neural network; a controller unit that obtains an average value Y1 of the absolute value of the output neuron before fixed-point and an average value Y2 of the absolute value of the output neuron after fixed-point; if Y1/Y2 is greater than a preset threshold K, obtaining the output neuron gradient precision of adjacent two layers of the multi-layer neural network, and obtaining an estimation value Aof error transfer precision; when Ais greater than a preset precision A, the output neuron gradient precision and weight precision of the adjacent two layers are increased; and an operation unit that represents the output neuron gradient and weight of the adjacent two layers according to the increased precision.

Neural network operation module and method

The present disclosure relates to a method, a device, and related products for processing data. In an embodiment of the present disclosure, when processing data related to a neural network, an optimal truncation threshold value for a plurality of pieces of data is determined. The data is truncated through the truncation data threshold, and the plurality of pieces of data is quantized from a high-precision format to a low-precision format. The method in the present disclosure can ensure the precision of data processing as high as possible while reducing the amount of data processing. In addition, the method also helps to significantly reduce the amount of data transmission, thereby greatly accelerating the data exchange among a plurality of computing devices.

Data processing method and apparatus, and related product

The technical solution involves a board card including a storage component, an interface apparatus, a control component, and an artificial intelligence chip. The artificial intelligence chip is connected to the storage component, the control component, and the interface apparatus, respectively; the storage component is used to store data; the interface apparatus is used to implement data transfer between the artificial intelligence chip and an external device; and the control component is used to monitor a state of the artificial intelligence chip. The board card is used to perform an artificial intelligence operation.

Neural network quantization parameter determination method and related products

The present disclosure provides a signal processing device, including a signal collector, an instruction converter, and a processor. Examples of the present disclosure may achieve precise recognition of users' intentions and bring operational conveniences to users.

Signal processing device and related products

The present disclosure relates to a neural network quantization parameter determination method and related products. A board card in the related products includes a memory device, an interface device, a control device, and an artificial intelligence chip, in which the artificial intelligence chip is connected with the memory device, the control device, and the interface device respectively. The memory device is configured to store data, and the interface device is configured to transmit data between the artificial intelligence chip and an external device. The control device is configured to monitor the state of the artificial intelligence chip. The board card can be used to perform an artificial intelligence computation.

The present disclosure provides a computation method and product thereof. The computation method adopts a fusion method to perform machine learning computations. Technical effects of the present disclosure include fewer computations and less power consumption.

Growing community of inventors

Shanghai, China

Xishan Zhang