Patent ID: 11899742
Assignee: NATIONAL TSING HUA UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A quantization method based on a hardware of in-memory computing comprising:
performing a quantization parameter providing step to provide a quantization parameter, wherein the quantization parameter comprises a quantized input activation, a quantized weight and a splitting value;
performing a parameter splitting step to split the quantized weight and the quantized input activation into a plurality of grouped quantized weights and a plurality of grouped activations, respectively, according to the splitting value;
performing a multiply-accumulate step to execute a multiply-accumulate operation with one of the grouped quantized weights and one of the grouped activations, and then generate a convolution output;
performing a convolution quantization step to quantize the convolution output to a quantized convolution output according to a convolution target bit; and
performing a convolution merging step to execute a partial-sum operation with the quantized convolution output according to the splitting value, and then generate an output activation;
wherein the quantized input activation has a number of a plurality of input channels, the splitting value is obtained by calculating the number of the input channels and a control parameter, the splitting value is equal to the number of the input channels divided by the control parameter, and the splitting value is a positive integer and is greater than 1 and less than or equal to the number of the input channels;
wherein the hardware of in-memory computing comprises a memory unit and an analog-to-digital converter, the memory unit signally connected to the analog-to-digital converter, the memory unit is a resistive random access memory (ReRAM);
wherein the quantization method based on the hardware of in-memory computing is applied to a quantization system comprising:
a multiply-accumulate unit configured to execute the multiply-accumulate operation with one of the grouped quantized weights and one of the grouped activations so as to generate the convolution output; and
an adder configured to execute the partial-sum operation with the quantized convolution output according to the splitting value so as to generate the output activation.