A user-defined floating-point number and a calculation method and a hardware structure thereof
A calculation method and floating-point number technology, which is applied in the field of convolutional neural network, can solve the problems of inadaptability and high power consumption data representation, and achieve the effects of simplifying calculation, expanding parallelism, throughput and calculation density
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[0040] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that for those of ordinary skill in the art, several changes and improvements can be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.
[0041] A custom floating-point number provided according to the present invention is composed of an integer part and a shared exponent. The integer part is composed of 1 sign bit and Z-1 mantissa bit. Z represents the number of digits of the integer part, and the shared exponent is 8 bits. The shared exponent has the same bit width as a single-precision floating point number.
[0042] Specifically, when the original data is quantized from a single-precision floating-point format to a custom...
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