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公开(公告)号:US20250077469A1
公开(公告)日:2025-03-06
申请号:US18025026
申请日:2022-06-28
Inventor: Xiaoping YAN , Chao TIAN
Abstract: The present disclosure provides a method of voice chip implementation, a voice chip, an intelligent voice product, an electronic device, and a storage medium, and relates to the field of artificial intelligence (AI) such as intelligent voice and AI chips. The method may include: constructing a voice chip including a first Digital Signal Processor (DSP) and a second DSP, the first DSP and the second DSP corresponding to a same Digital Signal Processor core Identifier (DSP core IP) and adopting heterogeneous designs; and completing a chip processing function in a corresponding intelligent voice product by using the voice chip, wherein different functions are completed by using the first DSP and the second DSP respectively. By use of the solutions of the present disclosure, implementation costs can be reduced.
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公开(公告)号:US20220292337A1
公开(公告)日:2022-09-15
申请号:US17832303
申请日:2022-06-03
Inventor: Chao TIAN , Lei JIA , Xiaoping YAN , Junhui WEN , Guanglai DENG , Qiang LI
Abstract: A neural network processing method, a neural network processing unit (NPU) and a processing device are provided. The method includes: obtaining by a quantizing unit in the NPU float type input data, quantizing the float type input data to obtain quantized input data, and providing the quantized input data to an operation unit; performing by the operation unit of the NPU a matrix-vector operation and/or a convolution operation to the quantized input data to obtain an operation result of the quantized input data; and performing by the quantizing unit inverse quantization to the operation result output by the operation unit to obtain an inverse quantization result.
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公开(公告)号:US20220138528A1
公开(公告)日:2022-05-05
申请号:US17526755
申请日:2021-11-15
Inventor: Chao TIAN , Lei JAI , Junhui WEN , Qiang LI
Abstract: A data processing method for a neural network accelerator, an electronic device and a storage medium are provided. The technical solution includes: obtaining data to be processed and an operation to be executed; obtaining a real-number full-connection operation corresponding to the operation to be executed; and performing the real-number full-connection operation on the data based on a real-number full-connection unit of the neural network accelerator to obtain a result of the operation to be executed for the data.
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