Reconfigurable integer-floating point adder

Abstract

The invention discloses a reconfigurable integer-floating point adder, and the adder comprises an enabling control module which is used for controlling and switching an operation mode of the reconfigurable integer-floating point adder: an integer mode or a floating point mode; an integer-floating point conversion module which is used for carrying out homogenization pretreatment on the input integer data or floating point data; a shift order matching module which is used for performing shift judgment and shift order matching operation on the data preprocessed by the integer-floating point conversion module in a floating point operation mode; a reconfigurable summation module which is used for completing summation operation of the processed integer or floating point data to obtain a preliminary summation result; and a pilot '0' counting shifter which is used for performing normalization processing on the preliminary summation result of summation in the floating point operation mode and outputting a final result. The method has better universality when meeting the operation requirement of simultaneously processing various types of data, and meanwhile, the flexibility of hardware distribution is improved, so that the layout area is reduced, the power consumption is reduced, and the hardware cost is reduced.

Description

technical field [0001] The invention belongs to the field of digital signal processing, in particular to a reconfigurable integer-floating point adder. Background technique [0002] In many computing-intensive applications, convolutional neural networks, as a typical multi-layer neural network, have always been at the core of research. In the research and design of deep learning chips, the accurate description of the model often requires the cooperation of various types of data and algorithms, and the arithmetic logic unit often needs to deal with the challenge of quickly processing multiple types of data. [0003] Usually, in the field of digital signal processing, multiplication and addition operations are widely used in operations such as convolution, that is, the result of the multiplication operation is fused with another operand to obtain the final result, thereby saving the execution delay of the entire multiplication and addition operation. The performance of multip...

AI Technical Summary

Problems solved by technology

However, the existing adders generally only support a single fixed-point addition or floating-point addition operation

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