Shanghai Cambricon Information Technology Co., Ltd

Cambricon Technologies Corporation Limited

Cambricon (xi'an) Semiconductor Co., Ltd.

Daofu Liu

Shaoli Liu

Lei Zhang

Tianshi Chen

Yunji Chen

Xiao Lin Zhang

Shuai Hu

Zidong Du

Zai Wang

Xiaofu Meng

Xuda Zhou

Yimin Zhuang

Jie Wei

Xiaobing Chen

Xiao Zhang

Xin Yu

Xiaobin Chen

Shiyi Zhou

The present disclosure relates to a data quantization processing method and apparatus, an electronic device, and a storage medium. The apparatus includes a control unit having an instruction caching unit, an instruction processing unit, and a storage queue unit. The instruction caching unit is configured to store a calculation instruction associated with an artificial neural network operation, the instruction processing unit is configured to parse the calculation instruction to obtain a plurality of operation instructions, and the storage queue unit is configured to store an instruction queue. The instruction queue includes a plurality of operation instructions or calculation instructions to be executed in an order of the queue. The above-mentioned method improves the operation precision of related products during a neural network model operation.

Data quantization processing method and apparatus, electronic device and storage medium

The application provides an operation method and device. Quantized data is looked up to realize an operation, which simplifies the structure and reduces the computation energy consumption of the data, meanwhile, a plurality of operations are realized.

Processing method and device, operation method and device

The present application provides an operation device and related products. The operation device is configured to execute operations of a network model, wherein the network model includes a neural network model and/or non-neural network model; the operation device comprises an operation unit, a controller unit and a storage unit, wherein the storage unit includes a data input unit, a storage medium and a scalar data storage unit. The technical solution provided by this application has advantages of a fast calculation speed and energy-saving.

Sparse processing in neural network processors

The application provides a processing method and device. Weights and input neurons are quantized respectively, and a weight dictionary, a weight codebook, a neuron dictionary, and a neuron codebook are determined. A computational codebook is determined according to the weight codebook and the neuron codebook. Meanwhile, according to the application, the computational codebook is determined according to two types of quantized data, and the two types of quantized data are combined, which facilitates data processing.

Processing method and device

Aspects for vector operations in neural network are described herein. The aspects may include a controller unit configured to receive an instruction to generate a random vector that includes one or more elements. The instruction may include a predetermined distribution, a count of the elements, and an address of the random vector. The aspects may further include a computation module configured to generate the one or more elements, wherein the one or more elements are subject to the predetermined distribution.

Apparatus and methods for generating random vectors

Aspect for storage device with fault tolerance capability for neural networks are described herein. The aspects may include a first storage unit of a storage device. The first storage unit is configured to store one or more first bits of data and the data includes floating point type data and fixed point type data. The first bits include one or more sign bits of the floating point type data and the fixed point type data. The aspect may further include a second storage unit of the storage device. The second storage unit may be configured to store one or more second bits of the data. In some examples, the first storage unit may include an ECC memory and the second storage unit may include a non-ECC memory. The ECC memory may include an ECC check Dynamic Random Access Memory and an ECC check Static Random Access Memory.

Storage device and methods with fault tolerance capability for neural networks

Aspects for vector circular shifting in neural network are described herein. The aspects may include a direct memory access unit configured to receive a vector that includes multiple elements. The multiple elements are stored in a one-dimensional data structure. The direct memory access unit may store the vector in a vector caching unit. The aspects may further include an instruction caching unit configured to receive a vector shifting instruction that includes a step length for shifting the elements in the vector. Further still, the aspects may include a computation module configured to shift the elements of the vector toward one direction by the step length.

Apparatus and methods for circular shift operations

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Pudong New Area, China

Daofu Liu